Real-Time Detection of Mango Based on Improved YOLOv4

Abstract

Agricultural mechanization occupies a key position in modern agriculture. Aiming at the fruit recognition target detection part of the picking robot, a mango recognition method based on an improved YOLOv4 network structure is proposed, which can quickly and accurately identify and locate mangoes. The method improves the recognition accuracy of the width adjustment network, then reduces the ResNet (Residual Networks) module to adjust the neck network to improve the prediction speed, and finally adds CBAM (Convolutional Block Attention Module) to improve the prediction accuracy of the network. The newly improved network model is YOLOv4-LightC-CBAM. The training results show that the mAP (mean Average Precision) obtained by YOLOV4-LightC-CBAM is 95.12%, which is 3.93% higher than YOLOv4. Regarding detection speed, YOLOV4-LightC-CBAM is up to 45.4 frames, which is 85.3% higher than YOLOv4. The results show that the modified network can recognize mangoes better, faster, and more accurately.