An improved YOLOv5s model using feature concatenation with attention mechanism for real-time fruit detection and counting

Abstract

An improved YOLOv5s model was proposed and validated on a new fruit dataset to solve the real-time detection task in a complex environment. With the incorporation of feature concatenation and an attention mechanism into the original YOLOv5s network, the improved YOLOv5s recorded 122 layers, 4.4 × 106 params, 12.8 GFLOPs, and 8.8 MB weight size, which are 45.5%, 30.2%, 14.1%, and 31.3% smaller than the original YOLOv5s, respectively. Meanwhile, the obtained 93.4% of mAP tested on the valid set, 96.0% of mAP tested on the test set, and 74 fps of speed tested on videos using improved YOLOv5s is 0.6%, 0.5%, and 10.4% higher than the original YOLOv5s model, respectively. Using videos, the fruit tracking and counting tested on the improved YOLOv5s observed less missed and incorrect detections compared to the original YOLOv5s. Furthermore, the aggregated detection performance of improved YOLOv5s outperformed the network of GhostYOLOv5s, YOLOv4-tiny, and YOLOv7-tiny, including other mainstream YOLO variants. Therefore, the improved YOLOv5s is lightweight with reduced computation costs, can better generalize against complex conditions, and is applicable for real-time detection in fruit picking robots and low-power devices.

A simplified network topology for fruit detection, counting and mobile-phone deployment

“…Gai et al [26] documented a cherry detection of 0.08 and 0.03 for F 1 -score higher than the YOLOv4 and YOLOv5, respectively, with the proposed YOLOv5s-cherry. Lawal et al [27] used feature concatenation with coordinate attention mechanism (CAM) (Hou et al [28]) to improved YOLOv5s for fruit detection. The improved YOLOv5s based on mean AP and speed recorded 0.6% and 10.4% respectively higher than the original YOLOv5s network.…”