Citrus Detection Method Based on Improved YOLOv5 Lightweight Network

Aiming at the problems of the existing citrus detection algorithms, such as low accuracy, large amount of model parameters, poor real-time detection, and unsuitability for mobile picking equipment, a citrus detection method based on the improved lightweight model YOLO-DoC is proposed. This paper introduces the ShuffleNetV2 network of the Bottleneck structure as the YOLOv5 backbone network model to construct a lightweight network. At the same time, the nonparametric SimAM attention mechanism is added to improve the recognition accuracy of targets in complex environments. In order to improve the positioning accuracy of the bounding box of the target fruit by the detection network, the bounding box of the target is obtained by introducing the method of Alpha-IoU bounding box regression loss function. Experiments show that the P (precision) value and mAP (mean average precision) value of the YOLO-DoC model are 98.8% and 99.1%, respectively, and the number of parameters is reduced to 1/7 that of the YOLOv5 network, and the size of the model is 2.8 MB. Compared with the original network model, the improved model has the advantages of fast recognition speed, high positioning accuracy and less memory usage. It can improve the picking efficiency under the premise of meeting the requirements of precise picking work. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.