YOLOv5s object detection method based on separable convolution and SENet

Author:

Li Yang1ORCID,Jia Yaodong1ORCID

Affiliation:

1. Changchun University of Science and Technology, China

Publisher

ACM

Reference18 articles.

1. MFC-Net : Multi-feature fusion cross neural network for salient object detection

2. GIR SHICK R, DONA HUE J, DARRELL T, Rich feature hierarchies for accurate object detection and semantic seg mentation [C] ∥Proceedings of the IEEE conference on computer vision and pattern recognition. Columbus: IEEE, 2020:580-587.

3. GIRSHICK R. Fast R-CNN [J]. Computer Science 2015.

4. Ren SHA O QING, He KAI MING, Girshick Ross, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6):1137-1149.

5. Dai, G., Hu, L., & Fan, J. (2022). DA-ActNN-YOLOV5: Hybrid YOLOv5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification. Computational Intelligence and Neuroscience, 2022.

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