Object Detection Algorithm Based on Alternate-Attention
The one-stage object detection algorithm, YOLOv3, has a fast detection speed and can meet real-time requirements. But giving the same attention weight to all grids during detection will result in the inability to highlight the detection subject, so the positioning accuracy of the bounding box is sti...
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| Vydáno v: | 2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) s. 376 - 381 |
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01.11.2021
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| Abstract | The one-stage object detection algorithm, YOLOv3, has a fast detection speed and can meet real-time requirements. But giving the same attention weight to all grids during detection will result in the inability to highlight the detection subject, so the positioning accuracy of the bounding box is still room for improvement. In order to improve the detection accuracy, this paper proposes an Alternate-Attention mechanism, using the global pooled attention mechanism to highlight the overall characteristics, and the self-attention mechanism to reflect the self-weight relationship between features. The two attention mechanisms are alternated and applied to the two dimensions of channel and space, and finally enhance the features extracted by Darknet-53. Experiments on the PASCAL VOC2007 dataset shows that this algorithm can effectively improve the detection accuracy. Compared with Faster RCNN, YOLO series and SSD series algorithms, the mAPlouo.5 value of this algorithm is higher, up to 80.24. |
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| AbstractList | The one-stage object detection algorithm, YOLOv3, has a fast detection speed and can meet real-time requirements. But giving the same attention weight to all grids during detection will result in the inability to highlight the detection subject, so the positioning accuracy of the bounding box is still room for improvement. In order to improve the detection accuracy, this paper proposes an Alternate-Attention mechanism, using the global pooled attention mechanism to highlight the overall characteristics, and the self-attention mechanism to reflect the self-weight relationship between features. The two attention mechanisms are alternated and applied to the two dimensions of channel and space, and finally enhance the features extracted by Darknet-53. Experiments on the PASCAL VOC2007 dataset shows that this algorithm can effectively improve the detection accuracy. Compared with Faster RCNN, YOLO series and SSD series algorithms, the mAPlouo.5 value of this algorithm is higher, up to 80.24. |
| Author | Qi, Haoru He, Xuejie Bai, Chenyan Liu, Honghong |
| Author_xml | – sequence: 1 givenname: Xuejie surname: He fullname: He, Xuejie email: 219100201O@cnu.edu.cn organization: Capital Normal University, College of Information Engineering,Beijing,China – sequence: 2 givenname: Chenyan surname: Bai fullname: Bai, Chenyan email: cybai@cnu.edu.cn organization: Capital Normal University, College of Information Engineering,Beijing,China – sequence: 3 givenname: Haoru surname: Qi fullname: Qi, Haoru organization: Capital Normal University, College of Information Engineering,Beijing,China – sequence: 4 givenname: Honghong surname: Liu fullname: Liu, Honghong organization: Capital Normal University, College of Information Engineering,Beijing,China |
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| Snippet | The one-stage object detection algorithm, YOLOv3, has a fast detection speed and can meet real-time requirements. But giving the same attention weight to all... |
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| StartPage | 376 |
| SubjectTerms | channel attention Computational modeling Computer science Feature extraction Object detection Real-time systems spatial attention YOLOv3 algorithm |
| Title | Object Detection Algorithm Based on Alternate-Attention |
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