Apple target recognition method in complex environment based on improved YOLOv4

摘要

In order to solve the problem of accurate recognition of apples in complex environments, this article proposes an apple recognition method based on improved YOLOv4, which can accurately locate and recognize apples in a variety of complex environments. This method uses the lightweight EfficientNet-B0 network as the feature extraction network for apple recognition and then combines with the PANet (Path Aggregation Network) network to fuse the features in the adjacent feature layers, which improves the recognition accuracy of fruit targets. In the experiment, the apples under bagging, nonbagging and night environment are identified. The results of the experiment show that the average precision of the improved YOLOv4 method is 93.42%, the recall rate is 87.64%, and the F1 value is 0.9035, which is about 2% lower than that of the original YOLOv4. However, the storage memory required for the model trained on the improved YOLOv4 is decreased by 87.8% compared with the original YOLOv4, which is only 29.8 MB, and the recognition speed is increased by 43%, reaching 63.20 frames per second. The recognition accuracy and speed are much higher than that of the two-stage Faster-RCNN. The recognition accuracy is equivalent to that of Mobilenetv3-YOLOv4, but the model of our method is smaller and the recognition speed is faster. Practical Applications Achieving rapid and accurate recognition of apples in complex environments is one of the key technologies for automatic fruit picking by picking robots, and it is also an important method and means for realizing orchard yield estimation. In order to recognize apples in the orchard environment more quickly and effectively, a light-weight YOLOv4 target recognition method based on deep learning is proposed. The method proposed in this article can significantly reduce the storage space and computing power requirements of the device. On the basis of maintaining high accuracy, it can achieve real-time and accurate recognition of apples in complex environments, and has great application value and practical significance for picking robot.