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学者姓名：王李进
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Abstract ：
Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores-95.3% and 89.5%, respectively-surpassing previous benchmarks. Additionally, we tested the model's transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management.
Keyword ：
attention mechanism attention mechanism deep learning deep learning weed detection weed detection YOLOv5s YOLOv5s
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| GB/T 7714 | Zhou, Qi , Li, Huicheng , Cai, Zhiling et al. YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection [J]. | SENSORS , 2025 , 25 (5) . |
| MLA | Zhou, Qi et al. "YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection" . | SENSORS 25 . 5 (2025) . |
| APA | Zhou, Qi , Li, Huicheng , Cai, Zhiling , Zhong, Yiwen , Zhong, Fenglin , Lin, Xiaoyu et al. YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection . | SENSORS , 2025 , 25 (5) . |
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In recent years, multi-objective particle swarm optimization algorithms have been widely used in science and engineering due to their advantages of fast convergence speed and easy implementation. However, the selection of globally optimal particle is an important and challenging problem in the design of multi-objective particle swarm optimization algorithms. In this regard, this paper proposes an adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update, named ADMOPSO. First, an adaptive penalty-based boundary intersection (PBI) distance strategy is designed to select the globally optimal particle from two elite particles which are randomly chosen from an elite particle set. This strategy better balances the diversity and convergence requirements of particle swarm optimization algorithm in the optimization process. Second, a simple position probabilistic update strategy is constructed to rewrite the velocity update method with the weight and use the learning rate to control the scale of the updated velocity in the position update equation to avoid particle swarm falling into the local optimum. Finally, an extensive experimental study is conducted to test the performance of several selected multi-objective optimization algorithms on ZDT, WFG and DTLZ benchmark problems, as well as 7 real-world problems were conducted to test the proposed algorithm. Comparative experimental results show that the algorithm proposed in this paper has significant advantages over other algorithms. This shows that the ADMOPSO algorithm is competitive in dealing with multi-objective problems.
Keyword ：
Adaptive distance Adaptive distance Competitive mechanism Competitive mechanism Multi-objective optimization Multi-objective optimization Particle swarm optimization algorithm Particle swarm optimization algorithm Position update Position update
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| GB/T 7714 | Wang, Liangying , Hong, Lihuan , Fu, Haoxuan et al. Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update [J]. | SWARM AND EVOLUTIONARY COMPUTATION , 2025 , 94 . |
| MLA | Wang, Liangying et al. "Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update" . | SWARM AND EVOLUTIONARY COMPUTATION 94 (2025) . |
| APA | Wang, Liangying , Hong, Lihuan , Fu, Haoxuan , Cai, Zhiling , Zhong, Yiwen , Wang, Lijin . Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update . | SWARM AND EVOLUTIONARY COMPUTATION , 2025 , 94 . |
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Pears are one of the most widely consumed fruits, and their quality directly impacts consumer satisfaction. Surface defects, such as black spots and minor blemishes, are crucial indicators of pear quality, but it is still challenging to detect them due to the similarity in visual features. This study presents PearSurfaceDefects, a self-constructed dataset, containing 13,915 images across six categories, with 66,189 bounding box annotations. These images were captured using a custom-built image acquisition platform. A comprehensive novel benchmark of 27 state-of-the-art YOLO object detectors of seven versions Scaled-YOLOv4, YOLOR, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and YOLOv9,has been established on the dataset. To further ensure the comprehensiveness of the evaluation, three advanced non YOLO object detection models, T-DETR, RT-DERTV2, and D-FINE, were also included. Through experiments, it was found that the detection accuracy of YOLOv4-P7 at mAP@0.5 reached 73.20%, and YOLOv5n and YOLOv6n also show great potential for real-time pear surface defect detection, and data augmentation can further improve the accuracy of pear surface defect detection. The pear surface defect detection dataset and software program code for model benchmarking in this study are both public, which will not only promote future research on pear surface defect detection and grading, but also provide valuable resources and reference for other fruit big data and similar research.
Keyword ：
computer vision computer vision dataset dataset deep learning deep learning pear pear pear surface defect detection pear surface defect detection smart agriculture smart agriculture
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| GB/T 7714 | Chen, Junsheng , Fu, Haoxuan , Lin, Chuhan et al. YOLOPears: a novel benchmark of YOLO object detectors for multi-class pear surface defect detection in quality grading systems [J]. | FRONTIERS IN PLANT SCIENCE , 2025 , 16 . |
| MLA | Chen, Junsheng et al. "YOLOPears: a novel benchmark of YOLO object detectors for multi-class pear surface defect detection in quality grading systems" . | FRONTIERS IN PLANT SCIENCE 16 (2025) . |
| APA | Chen, Junsheng , Fu, Haoxuan , Lin, Chuhan , Liu, Xian , Wang, Lijin , Lin, Yaohua . YOLOPears: a novel benchmark of YOLO object detectors for multi-class pear surface defect detection in quality grading systems . | FRONTIERS IN PLANT SCIENCE , 2025 , 16 . |
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Compared with conventional targets, small objects often face challenges such as smaller size, lower resolution, weaker contrast, and more background interference, making their detection more difficult. To address this issue, this paper proposes an improved small object detection method based on the YOLO11 model-PC-YOLO11s. The core innovation of PC-YOLO11s lies in the optimization of the detection network structure, which includes the following aspects: Firstly, PC-YOLO11s has adjusted the hierarchical structure of the detection network and added a P2 layer specifically for small object detection. By extracting the feature information of small objects in the high-resolution stage of the image, the P2 layer helps the network better capture small objects. At the same time, in order to reduce unnecessary calculations and lower the complexity of the model, we removed the P5 layer. In addition, we have introduced the coordinate spatial attention mechanism, which can help the network more accurately obtain the spatial and positional features required for small targets, thereby further improving detection accuracy. In the VisDrone2019 datasets, experimental results show that PC-YOLO11s outperforms other existing YOLO-series models in overall performance. Compared with the baseline YOLO11s model, PC-YOLO11s mAP@0.5 increased from 39.5% to 43.8%, mAP@0.5:0.95 increased from 23.6% to 26.3%, and the parameter count decreased from 9.416M to 7.103M. Not only that, we also applied PC-YOLO11s to tea bud datasets, and experiments showed that its performance is superior to other YOLO-series models. Experiments have shown that PC-YOLO11s exhibits excellent performance in small object detection tasks, with strong accuracy improvement and good generalization ability, which can meet the needs of small object detection in practical applications.
Keyword ：
attention mechanism attention mechanism small object detection small object detection tea bud tea bud VisDrone2019 VisDrone2019 YOLO11 YOLO11
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| GB/T 7714 | Wang, Zhou , Su, Yuting , Kang, Feng et al. PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection [J]. | SENSORS , 2025 , 25 (2) . |
| MLA | Wang, Zhou et al. "PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection" . | SENSORS 25 . 2 (2025) . |
| APA | Wang, Zhou , Su, Yuting , Kang, Feng , Wang, Lijin , Lin, Yaohua , Wu, Qingshou et al. PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection . | SENSORS , 2025 , 25 (2) . |
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In the field of object detection, enhancing algorithm performance in complex scenarios represents a fundamental technological challenge. To address this issue, this paper presents an efficient optimized YOLOv8 model with extended vision (YOLO-EV), which optimizes the performance of the YOLOv8 model through a series of innovative improvement measures and strategies. First, we propose a multi-branch group-enhanced fusion attention (MGEFA) module and integrate it into YOLO-EV, which significantly boosts the model's feature extraction capabilities. Second, we enhance the existing spatial pyramid pooling fast (SPPF) layer by integrating large scale kernel attention (LSKA), improving the model's efficiency in processing spatial information. Additionally, we replace the traditional IOU loss function with the Wise-IOU loss function, thereby enhancing localization accuracy across various target sizes. We also introduce a P6 layer to augment the model's detection capabilities for multi-scale targets. Through network structure optimization, we achieve higher computational efficiency, ensuring that YOLO-EV consumes fewer computational resources than YOLOv8s. In the validation section, preliminary tests on the VOC12 dataset demonstrate YOLO-EV's effectiveness in standard object detection tasks. Moreover, YOLO-EV has been applied to the CottonWeedDet12 and CropWeed datasets, which are characterized by complex scenes, diverse weed morphologies, significant occlusions, and numerous small targets. Experimental results indicate that YOLO-EV exhibits superior detection accuracy in these complex agricultural environments compared to the original YOLOv8s and other state-of-the-art models, effectively identifying and locating various types of weeds, thus demonstrating its significant practical application potential.
Keyword ：
attention mechanism attention mechanism complex environments complex environments efficient computing efficient computing object detection object detection YOLOv8 YOLOv8
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| GB/T 7714 | Zhou, Qi , Wang, Zhou , Zhong, Yiwen et al. Efficient Optimized YOLOv8 Model with Extended Vision [J]. | SENSORS , 2024 , 24 (20) . |
| MLA | Zhou, Qi et al. "Efficient Optimized YOLOv8 Model with Extended Vision" . | SENSORS 24 . 20 (2024) . |
| APA | Zhou, Qi , Wang, Zhou , Zhong, Yiwen , Zhong, Fenglin , Wang, Lijin . Efficient Optimized YOLOv8 Model with Extended Vision . | SENSORS , 2024 , 24 (20) . |
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关系网络是通过度量分析样本之间相似性的小样本分类方法，其固有的局部连通性限制了对样本全局特征的利用，并且在数据量较少时，模型的泛化能力不足.提出一种混合自监督学习和自适应感知关系网络的小样本分类方法.首先，通过结合自监督的实例级和场景级辅助任务、有监督的小样本分类辅助任务和自适应双相关注意任务提升模型特征表示和泛化能力.其次，引入动态权重平均策略，用于自适应优化辅助任务之间的权重.实例级辅助任务用于学习旋转样本未知类别的转移知识，场景级辅助任务确保不同旋转数据集的分类器预测结果一致性，小样本分类辅助任务则对扩展数据集进行有监督的分类预测平均，优化分类效能.自适应感知关系网络任务通过自适应层对图像特征变化进行自动调节，通过双关联注意力机制增强特征间相互作用，促进关键特征辨识.在数据集miniImageNet、tieredImageNet和CUB-200-2011上进行了验证，提出的方法在不同的骨干网络上都能较好地提升关系网络的分类性能，表明该方法是可行有效的.
Keyword ：
动态权重平均 动态权重平均 双关联注意力机制 双关联注意力机制 小样本分类 小样本分类 度量学习 度量学习 自监督学习 自监督学习 自适应感知关系网络 自适应感知关系网络
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| GB/T 7714 | 戴心杰 , 郑家杰 , 袁远飞 et al. 基于自监督与自适应感知关系网络的小样本图像分类 [J]. | 南京师范大学学报(工程技术版) , 2024 , 24 (04) : 68-78 . |
| MLA | 戴心杰 et al. "基于自监督与自适应感知关系网络的小样本图像分类" . | 南京师范大学学报(工程技术版) 24 . 04 (2024) : 68-78 . |
| APA | 戴心杰 , 郑家杰 , 袁远飞 , 王李进 , 吴清寿 . 基于自监督与自适应感知关系网络的小样本图像分类 . | 南京师范大学学报(工程技术版) , 2024 , 24 (04) , 68-78 . |
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To address the challenge of rapid mushroom identification in natural environments, this study proposes a mushroom identification model based on a feature fusion network, using nine types of mushrooms as research objects. The proposed model effectively integrates the advantages of traditional convolutional neural networks focusing on local features and Transformer networks emphasizing global features, realizing the simultaneous use of local and global features in image classification tasks. By improving the adaptive self-attention head count, the model's classification performance is further enhanced. The findings of this study hold considerable application value. When combined with deployment on mobile devices, they can contribute to significantly reducing the risk of food poisoning due to accidental mushroom ingestion. © 2024 IEEE.
Keyword ：
Convolutional neural networks Convolutional neural networks Image classification Image classification
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| GB/T 7714 | Dai, Xinjie , Lu, Xuanzhao , Wang, Lijin et al. Mushroom Recognition Based on ResNet-VIT Feature Fusion Network [C] . 2024 . |
| MLA | Dai, Xinjie et al. "Mushroom Recognition Based on ResNet-VIT Feature Fusion Network" . (2024) . |
| APA | Dai, Xinjie , Lu, Xuanzhao , Wang, Lijin , Wu, Qingshou . Mushroom Recognition Based on ResNet-VIT Feature Fusion Network . (2024) . |
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Cuckoo search algorithm contains two parameters called scaling factor and discovery probability. They are usually use as constants, resulting in sensitivity to problems. To this end, we attempt to use normal distribution random numbers to set scaling factor and discovery probability, proposing cuckoo search algorithm with normal distribution called NCS, to enhance the CS algorithm's refinement and convergency, and applying it to lychee image segmentation. Extensive experimental results conducted on 20 benchmark functions show the competition of NCS. In addition, NCS is applied to segment the lychee images, and achieves the improved performance. © 2023 IEEE.
Keyword ：
Image enhancement Image enhancement Image segmentation Image segmentation Learning algorithms Learning algorithms Normal distribution Normal distribution Optimization Optimization
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| GB/T 7714 | Yuan, Yuanfei , Wang, Liangying , Zhou, Qi et al. Cuckoo Search Algorithm with Normal Distribution and Its Application in Lychee Image Segmentation [J]. | ICSAI 2023 - 9th International Conference on Systems and Informatics , 2023 . |
| MLA | Yuan, Yuanfei et al. "Cuckoo Search Algorithm with Normal Distribution and Its Application in Lychee Image Segmentation" . | ICSAI 2023 - 9th International Conference on Systems and Informatics (2023) . |
| APA | Yuan, Yuanfei , Wang, Liangying , Zhou, Qi , Xiao, Wen , Wang, Lijin , Zhong, Yiwen . Cuckoo Search Algorithm with Normal Distribution and Its Application in Lychee Image Segmentation . | ICSAI 2023 - 9th International Conference on Systems and Informatics , 2023 . |
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Existing multi-objective cuckoo search (MOCS) algorithms are based on either Pareto dominance or decomposition. However, when dealing with complex multi-objective problems (MOPs), the Pareto dominance-based algorithms face a decrease in selection pressure, and the decomposition-based algorithms easily gain poor distributions. The objective of this paper is to repurpose an indicator-based MOCS by combining improved diversity enhancement (IDE) and adaptive scaling factor (ASF) for MOPs. In the proposed algorithm, hyper volume is used as the indicator to guarantee better convergence and enough spread of the population. IDE chooses the large hypervolume to rebuild the parent population to compensate for the lack of population diversity. Additionally, ASF makes full use of individuals information to enhance the search ability of Levy component in cuckoo search. Comprehensive experiments on 31 benchmark functions including two classical suites ZDT, WFG, and one challenged suite proposed in CEC2019, as well as 8 real-world problems were conducted to test the proposed algorithm. Compared with several state-of-the-art multi-objective evolutionary algorithms, the effectiveness and efficiency of our proposed method were demonstrated by the results.
Keyword ：
Cuckoo search Cuckoo search Diversity enhancement Diversity enhancement Hypervolume Hypervolume Indicator-based Indicator-based Multi-objective optimization Multi-objective optimization
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| GB/T 7714 | Liang, Maomao , Wang, Liangying , Wang, Lijin et al. A hypervolume-based cuckoo search algorithm with enhanced diversity and adaptive scaling factor [J]. | APPLIED SOFT COMPUTING , 2023 , 151 . |
| MLA | Liang, Maomao et al. "A hypervolume-based cuckoo search algorithm with enhanced diversity and adaptive scaling factor" . | APPLIED SOFT COMPUTING 151 (2023) . |
| APA | Liang, Maomao , Wang, Liangying , Wang, Lijin , Zhong, Yiwen . A hypervolume-based cuckoo search algorithm with enhanced diversity and adaptive scaling factor . | APPLIED SOFT COMPUTING , 2023 , 151 . |
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Bruising is one of the key factors that causes postharvest losses, which decreases the economic efficiency of fruit. Nevertheless, the detection of bruises still relies mainly on manual work, which is strongly subjective with long labor time and low efficiency. Accordingly, it is necessary to design an efficient fruit bruise detection approach. Thermal imaging (TI) is a fast and effective nondestructive testing technology. However, the commonly applied thermal excitation TI-based bruise detection may lead to a decrease in the shelf life of the fruit. Therefore, this study uses apple as the research object, introduces cold excitation to improve the sensitivity of bruise detection, and then constructs a simple longwavelength infrared range (7.5-13 mu m) TI system to acquire the thermal image of bruised apples. In addition, the low signal-to-noise ratio of thermal images also leads to detection performance degradation. Thus, the YOLOv5s network is applied and improved to achieve better detection. The specific methods are described as follows: (1) Since the thermal images have the problem of duplicated RGB data, group convolution is used to reduce the feature duplication computation. (2) The bottleneck structure of YOLOv5s is replaced by the ghost bottleneck (GB), and the number of bottlenecks is reduced to decrease the computational quantity of extracting redundant features of thermal images. (3) The shrinkage module is inserted into the GB, and the threshold is automatically obtained through two fully connected layers without relevant professional knowledge to eliminate noise in the features that may cause performance degradation. The F2 score, mAP and mAP50 of the proposed model are 97.76%, 86.24% and 98.08%, respectively, which are better than those of YOLOv5s. Moreover, the computation and the FPS of the proposed model are 1.31 GFLOPs and 160, which are 31.95% and 121.21% of those of the YOLOv5s, respectively.
Keyword ：
Apple Apple Bruise detection Bruise detection Cold excitation Cold excitation Thermal imaging Thermal imaging YOLOv5s YOLOv5s
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| GB/T 7714 | Lin, Peijie , Yang, Hua , Cheng, Shuying et al. An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images [J]. | POSTHARVEST BIOLOGY AND TECHNOLOGY , 2023 , 199 . |
| MLA | Lin, Peijie et al. "An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images" . | POSTHARVEST BIOLOGY AND TECHNOLOGY 199 (2023) . |
| APA | Lin, Peijie , Yang, Hua , Cheng, Shuying , Guo, Feng , Wang, Lijin , Lin, Yaohai . An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images . | POSTHARVEST BIOLOGY AND TECHNOLOGY , 2023 , 199 . |
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