基于可形变卷积与SimAM注意力的密集柑橘检测算法

Dense citrus detection algorithm based on deformable convolution and SimAM attention

针对现有检测算法难以检测自然场景下小而密集的柑橘问题,提出一种DS-YOLO(Deformable Convolution SimAM YOLO)密集柑橘检测算法。引入可形变卷积网络(Deformable Convolution)代替原YOLOv4中的特征提取网络部分卷积层,使特征提取网络能自适应提取遮挡、重叠等导致柑橘形状信息缺失的位置特征,在特征融合模块中,增加新的检测尺度并融合SimAM注意力机制,增强模型对于小而密集柑橘特征的提取能力。试验结果表明:DS-YOLO算法相较于原YOLOv4准确率提高8.75%,召回率提高7.9%,F1分数提高5%,能够较准确检测自然环境下的密集柑橘目标,为密集水果产量预测和采摘机器人提供了有效的技术支持。

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.