Abstract
Addressing the challenges of high model complexity, low generalization capability, and suboptimal detection performance in most algorithms for crop leaf disease detection, the paper propose a lightweight enhanced YOLOv8 algorithm. First, by incorporating the advantages of GhostNet's feature redundancy reduction and MobileNet's ability to fuse diverse channel features using the concept of Group Convolution, the paper enhance the backbone network. This improves the network's ability to extract critical features from a multitude of similar redundant feature maps. Second, to improve detection accuracy while reducing model parameters and computational load, the paper introduce the slim-Neck module. Finally, addressing the issue where detection head parameters and computations account for over half of the model, the paper restructure the Head using the concept of shared parameters and integrate convolution blocks that enhance multi-scale information recognition. Results from multiple experiments, averaged for consistency, indicate that compared to the original YOLOv8 algorithm, the improved algorithm achieves an increase in mAP50 from 86% to 87.3% and mAP50:95 from 67% to 68.6%. The model's size is a mere 5.45 MB, and the computational parameter GFLOPs is reduced from 8.1 to 5.5, even lower than the most lightweight YOLOv5. In comparison to other large-model algorithms, this model also demonstrates strong competitiveness in detection accuracy.
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Wen, G., Li, M., Luo, Y. et al. The improved YOLOv8 algorithm based on EMSPConv and SPE-head modules. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-17957-4
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DOI: https://doi.org/10.1007/s11042-023-17957-4