Real-time detection method for infrared pedestrian

Jinling Chen; Xin Liu

doi:10.1117/12.2684921

19 October 2023 Real-time detection method for infrared pedestrian

Jinling Chen, Xin Liu

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127092I (2023) https://doi.org/10.1117/12.2684921
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

We propose a real-time infrared pedestrian detection method, which achieves higher performance than the state-of-theart YOLO series in infrared pedestrian detection. Our method is based on the YOLOv6-n with some new technologies. In particular, Mixed Downsampling Module (MDM) and Cross-Stage Connection Structure (CSCS) are designed to reduce the inference latency of the network. In addition, Simplified Spatial Attention Module (SimSAM) is proposed to improve detection performance with negligible overheads. We validate our method through extensive experiments on FLIR detection datasets. Experimental results show that our method can achieve 84.86% AP with inference latency of 0.54 ms on an NVIDIA 1080Ti GPU.

Citation Download Citation

Jinling Chen and Xin Liu "Real-time detection method for infrared pedestrian", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127092I (19 October 2023); https://doi.org/10.1117/12.2684921

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