An improved YOLOv7-tiny algorithm for dense pedestrian detection

Wentao Jiang; Zijian Wang

doi:10.1117/12.3038546

23 August 2024 An improved YOLOv7-tiny algorithm for dense pedestrian detection

Wentao Jiang, Zijian Wang

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132502Q (2024) https://doi.org/10.1117/12.3038546
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In view of the missing detection and false detection of the current pedestrian detection model in the dense pedestrian scene, a dense pedestrian detection algorithm based on Yolov7-tiny was proposed. In order to solve the problem of missing pedestrian target features in dense pedestrian detection scenarios, the dynamic convolution module and the improved ROELAN module are used in the backbone network to make the network focus on the important features of dense pedestrians through dynamic convolution, which effectively reduces the impact of feature loss on model detection. In order to solve the problem that small target pedestrians are easy to be missed in dense pedestrian detection scenarios, the BIFPN idea, CARAFE upsampling operator and the improved Rep-ELAN-B module are introduced to improve the detection performance of the model by using the small target feature information in the medium and low-dimensional feature maps. The average detection accuracy of the improved model reached 83.4%, which was 2.7% higher than that of the baseline model. The experimental results show that the proposed algorithm can be better applied to dense pedestrian detection scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wentao Jiang and Zijian Wang "An improved YOLOv7-tiny algorithm for dense pedestrian detection", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132502Q (23 August 2024); https://doi.org/10.1117/12.3038546

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KEYWORDS

Detection and tracking algorithms

Convolution

Feature fusion

Feature extraction

Education and training

Ablation

Performance modeling

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