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.

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.

This paper proposes a hybrid factor strategy for cuckoo search algorithm by combining constant factor and varied factor. The constant factor is used to the dimensions of each solution which are closer to the corresponding dimensions of the best solution, while the varied factor using a random or a chaotic sequence is utilized to farer dimensions. For each solution, the dimension whose distance to the corresponding one of the best solution is shorter than mean distance of all dimensional distances will be regarded as the closer one, otherwise as the farer one. A suit of 20 benchmark functions are employed to verify the performance of the proposed strategy, and the results show the improvement in effectiveness and efficiency of the hybridization.

To advance automatic tomato leaf disease detection in precision agriculture, this study addresses critical challenges in complex field environments, such as variable lesion scales, background interference, and deployment constraints. We propose MSP-Net, a task-driven detection framework with targeted architectural refinements integrating three specific optimizations. First, a Multi-Scale Perception Convolution Module (MSPCM) is introduced to capture diverse disease features across early-to-late infection stages. Second, SimAM-enhanced C3k2 layers are utilized to suppress background noise and focus on fine-grained lesion cues. Third, a Multi-Scale Feature Enhancement Module (MSFEM) bridges the semantic gap between shallow and deep features to improve fusion efficacy. Furthermore, we construct a lightweight variant, L-MSP-Net, using architectural migration and structured pruning for edge efficiency. Experimental results on the real-world Tomato-Village dataset show that MSP-Net achieves 92.0% mAP@0.5, outperforming the YOLOv11s baseline by 2.0%. L-MSP-Net attains 86.1% mAP@0.5, improving by 3.6% over the lightweight YOLOv11n baseline while reducing parameters by 10.5%, and is successfully deployed on the RK3588 edge platform. Additional cross-dataset experiments on PASCAL VOC and MS COCO evaluate the transferability of the proposed architectural refinements to generic object detection tasks.