YOLOv5s-CEDB: A robust and efficiency Camellia oleifera fruit detection algorithm in complex natural scenes

文献类型： 外文期刊

第一作者： Zhu, Aobin

作者： Zhu, Aobin;Chen, Liping;Zhu, Aobin;Zhang, Ruirui;Zhang, Linhuan;Yi, Tongchuan;Wang, Liwan;Zhang, Danzhu;Chen, Liping;Zhu, Aobin;Zhang, Ruirui;Zhang, Linhuan;Yi, Tongchuan;Wang, Liwan;Zhang, Danzhu;Chen, Liping;Zhu, Aobin;Zhang, Ruirui;Zhang, Linhuan;Yi, Tongchuan;Wang, Liwan;Zhang, Danzhu;Chen, Liping;Zhu, Aobin;Zhang, Ruirui;Zhang, Linhuan;Yi, Tongchuan;Wang, Liwan;Zhang, Danzhu;Chen, Liping

作者机构：

关键词： Camellia oleifera fruit; Natural scenes; Object detection; YOLOv5s

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 221 卷

页码：

收录情况： SCI

摘要： To solve the problem of poor recognition accuracy caused by various colors, uneven distribution, and occlusion by branches and leaves of Camellia oleifera fruits under natural growth conditions, this study proposes an improved deep learning network, YOLOv5s-CEDB, for Camellia oleifera fruit detection based on YOLOv5s. Coordinate Attention Mechanism (CoordAtt), Deformable Convolution (DConv), and Explicit Visual Center (EVC) are introduced to enhance the network's local and global feature extraction performance. To improve the detection performance for small and dense targets, the feature fusion module of the network was replaced with the designed light-bidirectional feature pyramid (light-BiFPN) structure. GhostConv was used to reduce the parameters and inference speed of the structure. A dataset with different light conditions, colors, and density levels of Camellia oleifera fruits was established, and performance evaluation experiments were conducted. Experimental results showed that the mean Average Precision (mAP) and F1-score of the designed YOLOv5sCEDB network reached 91.4 % and 89.6 %, respectively, which were 2.6 % and 3.6 % higher than those of the original YOLOv5s model, respectively, and the influencing frame rate arrived at 37.6 FPS. Under different colors, distribution densities, occlusion scenarios, and light intensities, the detection accuracy of the YOLOv5sCEDB network model was significantly better than those of the YOLOv5s, YOLOv8s and Faster-RCNN networks. It was verified that the proposed YOLOv5s-CEDB network could significantly improve the accuracy and stability of Camellia oleifera fruit detection, satisfying the requirements of yield estimation and intelligent harvesting.

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