Object Detection Method for Multi-scale Full-scene Surveillance Based on Attention Mechanism

ZHANG Dexiang; WANG Jun; YUAN Peicheng

doi:10.11999/JEIT210664

Volume 44 Issue 9

Sep. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(9): 3249-3257

ZHANG Dexiang, WANG Jun, YUAN Peicheng. Object Detection Method for Multi-scale Full-scene Surveillance Based on Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3249-3257. doi: 10.11999/JEIT210664

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ZHANG Dexiang, WANG Jun, YUAN Peicheng. Object Detection Method for Multi-scale Full-scene Surveillance Based on Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3249-3257. doi: 10.11999/JEIT210664

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Object Detection Method for Multi-scale Full-scene Surveillance Based on Attention Mechanism

doi: 10.11999/JEIT210664

1.
School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
2.
School of Electronic and Electrical Engineering, Anhui Sanlian University, Hefei 230601, China

Funds: The Foundation National Key Research and Development Program (2018YFB0504604)

Received Date: 2021-07-02
Accepted Date: 2022-01-12
Rev Recd Date: 2021-12-25

Available Online: 2022-02-03

Publish Date: 2022-09-19

Abstract

Abstract

Focusing on the problem that the object features are not obvious in complex urban surveillance scenes due to large object size changes, object occlusion and weather influence, a multi-scale full-scene surveillance object detection method based on attention mechanism is proposed. In this paper, a multi-scale detection network structure based on Yolov5s model is designed to improve the adaptability of the network to the changes of object size. Meanwhile, a feature extraction module based on attention mechanism is constructed to obtain channel level weight of features through network learning, which enhances the object features, suppresses the background features, and improves the network extraction capability of features. The initial anchor frame size of the full-scene surveillance dataset is calculated by the K-means clustering algorithm to accelerate the model convergence while improving the detection accuracy. On the COCO dataset, the mean Average Precision (mAP) is improved by 3.7%, and the mAP₅₀ is improved by 4.7% compared with the basic network, and the model inference time is only 3.8 ms. In the full-scene surveillance dataset, the mAP₅₀ reaches 89.6% and the fps is 154 frames per second when processing the surveillance video, which meets the real-time detection requirements of the surveillance scene.
- Object detection,
- Full-scene surveillance,
- Multi-scale detection,
- Attention mechanism

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References(27)

References

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