Real-time pineapple detection for agricultural robot via lightweight YOLOv7-tiny model

Abstract

Automated detection of pineapple-picking robots in complex agricultural environments is challenging due to uneven lighting and occluded fruit. In this paper, a lightweight pineapple detection model based on the YOLOv7-tiny model is presented for real-time accurate detection by agricultural robots. Firstly, The SIoU loss function is designed to substitute for the CIoU loss function in the initial model. The mismatched direction can be used to minimize the overall degree of freedom and expedite model convergence. Then, incorporating the CBAM module into the backbone network enhances the model's capacity to emphasize key features of the pineapple fruit, resulting in improved generalization ability and overall robustness. Ultimately, the improved YOLOv7-tiny model achieves a mAP@0.5 of 96.9%, surpassing the original model by 1.6%.