Real-time rotational obstacle detection-based intelligent safety management for construction machines

Minwoo Woo; Jong-Pil Yun; Hong-In Won; SeungHyun Jeong; Byeong Hak Kim

doi:10.1117/12.2665505

7 June 2023 Real-time rotational obstacle detection-based intelligent safety management for construction machines

Minwoo Woo, Jong-Pil Yun, Hong-In Won, SeungHyun Jeong, Byeong Hak Kim

Proceedings Volume 12571, Real-time Processing of Image, Depth and Video Information 2023; 125710M (2023) https://doi.org/10.1117/12.2665505
Event: SPIE Optics + Optoelectronics, 2023, Prague, Czech Republic

Conference Poster

Abstract

The number of industrial accidents has been recorded by construction cranes for a high proportion compared to other machines on construction sites. For this reason the technology for preventing collision between salvages and obstacles is strongly demanded. In this study, we propose an intelligent safety management method based on a rotational obstacle detection that detects obstacles around a crane by learning a private dataset acquired in an environment similar to an actual construction site. The rotational obstacle detection model of the proposed method is designed to more accurately predict obstacles around a crane using RGB video sequences images from the multi-domain dataset. It is composed of the real-time models for object detection, one of the typical one-stage detectors, and the self attention distillation (SAD) method. In the experimental results, its performance of accuracy over than 70% mAP. This study can be applied not only to cranes but also to other machines for safety monitoring systems on various domain fields.

Citation Download Citation

Minwoo Woo, Jong-Pil Yun, Hong-In Won, SeungHyun Jeong, and Byeong Hak Kim "Real-time rotational obstacle detection-based intelligent safety management for construction machines", Proc. SPIE 12571, Real-time Processing of Image, Depth and Video Information 2023, 125710M (7 June 2023); https://doi.org/10.1117/12.2665505

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