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GSA-DLA34: a novel anchor-free method for human-vehicle detection

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Abstract

Most anchor-free object detectors suffer from intersample imbalance, underutilization of multiscale features and long training times in traffic object dataset. As a result, the efficiency and accuracy of the detector may be significantly reduced for samples with few categories and small sizes. To address these problems, we propose a novel anchor-free approach, namely, GSA-DLA34, which is based on Gaussian kernel, sample weights, and attention. Its features are as follows. First, pyramid squeeze attention (PSA) is added after the backbone network to enhance multiscale traffic object representations. Second, for better object positioning with few categories and small scales, we design active sample weights for regression loss to make better information use. In addition, an elliptical Gaussian sampling module (EGSM) with a controllable Gaussian kernel shape is incorporated into the classification and regression branches to accelerate network training. The results show that our GSA-DLA34 has a significant advantage in balancing training time, inference speed, and accuracy. With an average precision of 89% on the PASCAL VOC dataset and an inference speed of 55.2 FPS on the RTX 2080 Ti, the GSA-DLA34 method can significantly improve human-vehicle recognition accuracy.

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Acknowledgements

This work is supported by the Liaoning Provincial Science and Technology Department (No.1655706734383).

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  1. School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian, 116028, China

    Xinying Chen, Na Lv, Shuo Lv & Hao Zhang

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Chen, X., Lv, N., Lv, S. et al. GSA-DLA34: a novel anchor-free method for human-vehicle detection. Appl Intell 53, 24619–24637 (2023). https://doi.org/10.1007/s10489-023-04788-x

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