Lightweight Field Cotton Grade Detection Based on YOLOv8

To address issues such as high false alarm and missed alarm rates in existing target detection algorithms owing to multi-scale variations in complex field cotton and large computational volume of existing detection algorithms, which make their deployment in edge devices challenging, a lightweight field cotton grade detection algorithm, YOLOv8-Cotton, is proposed. This algorithm optimizes feature extraction and fusion and combines model pruning and knowledge distillation techniques. First, a Multi-Scale Convolutional (MSConv) is designed in the feature extraction network, which contains convolutional kernels of different scales and can enhance the feature extraction capability of the network. Second, an Efficient Local Feature Selection (ELS) mechanism is constructed in the neck network to capture horizontal and vertical features in the spatial dimension and suppress irrelevant regions from affecting the prediction results. Then, a novel hierarchical feature fusion network, HL-PAN, is constructed using the ELS mechanism to utilize the complementary information generated by its Upsampling Selection Feature Fusion (U-SFF) and Downsampling Selection Feature Fusion (D-SFF) to guide the feature fusion, which enhances the ability of the model to detect multi-scale changes in cotton. Third, the model is compressed using the Layer-Adaptive Magnitude-based Pruning (LAMP) model pruning algorithm to reduce its weight. Finally, feature distillation is performed using the CWD loss function to enhance the detection performance of the lightweight model. Experimental results show that YOLOv8-Cotton achieves mAP@0.5 and mAP@0.5∶0.95 values of 75.4% and 53.1%, respectively, on the self-constructed dataset, which are 5.1 and 2.1 percentage point improvements over the baseline algorithm. Furthermore, the model size decreases by 4.83 MB and computation is reduced by 5.8×109. Additionally, the results show that the model can be generalized on a publicly available dataset.