Pedestrian detection and positioning for component hoisting based on millimeter wave radar and monocular vision sensor

He Ling; Jiduo Pan

doi:10.1117/12.2604521

5 October 2021 Pedestrian detection and positioning for component hoisting based on millimeter wave radar and monocular vision sensor

He Ling, Jiduo Pan

Author Affiliations +

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 1191109 (2021) https://doi.org/10.1117/12.2604521
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

Aiming at the problem that it is difficult to accurately detect and locate pedestrians due to high air dust concentration and low visibility at the construction site lifting equipment, a pedestrian detection and positioning method based on monocular vision combined with millimeter wave radar is proposed. This method inputs the RGB image obtained by the monocular camera into the YOLOv4 algorithm to achieve pedestrian target detection. The distance information of the obstacle obtained by the millimeter wave radar and the pixel coordinate information of the pedestrian obstacle target obtained by the monocular camera are used for information fusion, and then it based on the positioning principle of monocular vision realizes the spatial positioning of pedestrian targets. Experimental results show that the accuracy and recall rates of pedestrian detection reach 94.09% and 93.52%, respectively, which are higher than 90.79% and 89.13% of the YOLOv3 algorithm, and the detection speed reaches 65 FPS; the relative error of pedestrian positioning in the lateral distance is 3.52%. The method is accurate and rapid, and can better realize real-time pedestrian detection and positioning in the hoisting equipment site.

Citation Download Citation

He Ling and Jiduo Pan "Pedestrian detection and positioning for component hoisting based on millimeter wave radar and monocular vision sensor", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 1191109 (5 October 2021); https://doi.org/10.1117/12.2604521

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KEYWORDS

Target detection

Detection and tracking algorithms

Cameras

Extremely high frequency

Radar

Imaging systems

Sensors

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