YOLO-FSL: Method of Maturity Detection for Plum in Natural Environment Based on Improved YOLOv8
DOI:
https://doi.org/10.52151/jae2025621.1915Keywords:
Deep learning, lightweight model, mean average precision, object detection, pruning techniques, squeeze and excitation attention mechanismAbstract
This study introduces an improved YOLOv8 model YOLO-FSL for rapid detection of plum maturity under natural conditions. Firstly, the Faster Net Block module was introduced into the C2f module of the YOLOv8s backbone network, forming the C2f_Faster module; Secondly, squeeze and excitation (SE) attention mechanism after each C2f_Faster module was added in the backbone network; Finally, the improved YOLOv8s model was pruned using channel pruning algorithm to simplify the detection model and ensure detection efficiency. The experimental results show that the mean average precision (mAP) and model size of the improved YOLO-FSL model is 91.2% and 4.1 MB (Mega Byte), respectively, which reduces the number of parameters by 82.57% and improves the mAP by 1.6% compared with the original network YOLOv8s. Compared with the mainstream target detection algorithms of YOLOv3-tiny, YOLOv5s, YOLOv6s, and YOLOv7-Tiny, the mAP of the YOLO-FSL model is improved by 4.4%, 2.0%, 1.0%, and 1.6%, respectively. The YOLO-FSL model has a higher detection accuracy, and at the same time reduces the amount of computation and the number of parameters, and lowers the hardware dependence, which provides a technical reference for the automated picking of plums.
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