关键词: 目标检测, 特征提取, 密集柑橘, 可形变卷积, SimAM注意力

Abstract: Aiming at the problem that existing detection algorithms are difficult to detect small and dense citrus in natural scenes, a DS-YOLO (Deformable Convolution SimAM YOLO) algorithm for dense citrus detection is proposed. Deformable convolution is introduced to extract partial convolution layers of the network instead of the features in the original YOLOv4. The feature extraction network adaptively extracts the location features that result in missing citrus shape information, such as occlusion and overlap. In the feature fusion module, a new detection scale is added and the SimAM attention mechanism is fused to enhance the models ability to extract small and dense citrus features. The results show that the DS-YOLO algorithm improves the accuracy by 8.75%, recall by 7.9%, and F1 by 5% compared with the original YOLOv4 algorithm. It can detect dense citrus targets of the natural environment more accurately and provide effective technical support for dense fruit yield prediction and harvesting robots.

Key words: object detection, feature extraction, dense citrus, deformable convolution, SimAM attention