关键词: 未成熟青苹果, 改进YOLOv3, 目标检测, 自然场景

Abstract: In order to study the automatic bagging technology for apples suitable for the current production practice and realize the accurate recognition of immature green apples in the real environment, this paper proposed an image recognition method of immature green apples in natural scenes based on the improved YOLOv3. Firstly, in order to improve the recognition accuracy of immature green apples in images containing interference factors, this paper was based on the idea that YOLOv3 algorithm utilized residual network and multiscale feature fusion for detecting small targets, and improved and experimentally verified the YOLOv3 feature extraction network by utilizing the feature maps with dimensions of (104, 104, 128) instead of the original feature map with dimensions of (13, 13, 1 024) as the output. The improved YOLOv3 target detection model for immature green apples was proposed, which improved the ability of the algorithm network to capture immature green apples in the image and the recognition accuracy by increasing the size of the output feature maps of the feature extraction network and decreasing the size of the receptive field. Secondly, this paper designed the recognition comparison test under different algorithms, different varieties and different environments, and compared and analyzed the results. The mean Average Precision and Recall of the improved YOLOv3 on the overall dataset were 92.46% and 87.6%, respectively, which were 3.22% and 14.57% higher than that of the original YOLOv3. The performance enhancement of the improved model was mainly reflected in the ability to detect the correct number of targets. The mean Average Precision of the improved YOLOv3 on the test set of images containing the effects of illumination, overlapping and occlusion effects was improved by 3.58% and 2.74% compared to the original YOLOv3, respectively. The improved YOLOv3 model had higher detection accuracy on the overall dataset and on the test set of images containing interference factors, higher number of correct targets detected, and better antiinterference ability.

Key words: immature green apples, improved YOLOv3, target detection, natural scene