Multispectral Benchmark Dataset and Baseline for Forklift Collision Avoidance

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 20; p. 7953
• Main Authors: Kim, Hyeongjun, Kim, Taejoo, Jo, Won, Kim, Jiwon, Shin, Jeongmin, Han, Daechan, Hwang, Yujin, Choi, Yukyung
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022; MDPI
• Subjects: 2.5D detection; Accident prevention; Algorithms; automated forklifts; Automation; Cameras; Centroids; Collision avoidance; Datasets; Domains; Employee performance; Fork lift trucks; intralogistics; Localization; multispectral; Outdoors; pedestrian detection; Sensors; Surveillance
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22207953

In this paper, multispectral pedestrian detection is mainly discussed, which can contribute to assigning human-aware properties to automated forklifts to prevent accidents, such as collisions, at an early stage. Since there was no multispectral pedestrian detection dataset in an intralogistics domain, we collected a dataset; the dataset employs a method that aligns image pairs with different domains, i.e. RGB and thermal, without the use of a cumbersome device such as a beam splitter, but rather by exploiting the disparity between RGB sensors and camera geometry. In addition, we propose a multispectral pedestrian detector called SSD 2.5D that can not only detect pedestrians but also estimate the distance between an automated forklift and workers. In extensive experiments, the performance of detection and centroid localization is validated with respect to evaluation metrics used in the driving car domain but with distinct categories, such as hazardous zone and warning zone, to make it more applicable to the intralogistics domain.