基于深度学习的轻量化草莓成熟度检测模型OA

To achieve real-time detection of strawberry ripeness in complex environments,a lightweight object detection model YO-LOv5s-DCC based on an enhanced YOLOv5s architecture was proposed,aiming to enhance both accuracy and computational efficien-cy.Depthwise separable convolutions were embedded within the backbone structure to reduce computational complexity.The neck sec-tion incorporated CARAFE upsampling to enhance the capture of minute features,while the detection head integrated CBAM attention mechanisms.Dynamic weighting improved feature selection performance,resulting in an optimized model balancing detection accura-cy and lightweight efficiency.The refined model size was only 13.9 MB,achieving precision,recall,and mean average precision(mAP)of 93.4%,92.7%,and 95.3%,respectively.Compared to the original YOLOv5s model,precision,recall,and mAP improved by 1.3,0.9,and 1.6 percentage points,respectively,while reducing parameters by 0.5×106.Compared to mainstream lightweight mod-els such as YOLOX-s,YOLOv7-tiny,and YOLOv8s,YOLOv5s-DCC deliverd the best overall performance.It could meet the real-time strawberry ripeness detection requirements of agricultural harvesting robots in complex environments.