Green pepper recognition algorithm based on improved YOLOv7

In response to the problem of low recognition accuracy for green pepper picking in intelligent picking scenarios, an enhanced model based on the YOLOv7 object detection algorithm was proposed to elevate the recognition accuracy of green peppers. Firstly, the Self Attention mechanism is introduced to focus on the correlation between different positions, thereby capturing richer spatial and channel information of contextual information and reducing interference from other targets. Secondly, ASFF feature fusion module is added to adaptively integrate multi-scale features by learning weights to improve target recognition ability. Through training experiments on the established dataset, according to the test results, the evaluation accuracy rate (mAP) of the improved YOLOV7 model is 92.7%, which is an increase of 3.2% compared with the original algorithm. This result proved that the improved model showed higher efficiency in identifying green pepper.