Fast recognition method for citrus under complex environments based on improved YOLOv3

Abstract

Here, the improved multi-scale YOLO algorithm (Improved-YOLOv3) is presented, which was proposed to realize fast and accurate recognition of citrus fruit in a field environment. With the modification of the YOLO-styled network model, a darknet-53 backbone network with residual modules was designed. A multi-scale detection module was to construct a network model for rapid recognition of citrus fruit in complex environments. Using the improved model to detect and identify citrus fruit targets, the network model can extract more feature information. The improved YOLOv3 model was tested with citrus data, and the detection performance of the improved network and the influence of the number of backbone network layers on the feature extraction capability were compared. The results showed a good detection ability (detection rate, accuracy, map, detection speed) for the target fruit, and the improved YOLOv3 network showed higher accuracy. Moreover, the performance of different training models were compared: the Improved-YOLOv3 has stronger robustness, higher detection accuracy, and shorter training time, and can recognize citrus in complex field environments. The experiment showed that the precision of Improved-YOLOv3 was 90.5% and the accuracy reached 94.3%, the recall rate was 90.3%, the detection time was 9.89 ms per frame, which could provide technical support for this visual recognition system of citrus picking robot.