An Obstacle Detection Method Based on Longitudinal Active Vision

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 13; p. 4407
• Main Authors: Shi, Shuyue, Ni, Juan, Kong, Xiangcun, Zhu, Huajian, Zhan, Jiaze, Sun, Qintao, Xu, Yi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.07.2024
• Subjects: Accuracy; Algorithms; Automobile safety; camera rotation strategy; Cameras; Deep learning; distance estimation; image processing; longitudinal active vision; Neural networks; Semantics; Sensors; Traffic accidents & safety; Vehicles; image processing; distance estimation; longitudinal active vision; camera rotation strategy
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24134407

The types of obstacles encountered in the road environment are complex and diverse, and accurate and reliable detection of obstacles is the key to improving traffic safety. Traditional obstacle detection methods are limited by the type of samples and therefore cannot detect others comprehensively. Therefore, this paper proposes an obstacle detection method based on longitudinal active vision. The obstacles are recognized according to the height difference characteristics between the obstacle imaging points and the ground points in the image, and the obstacle detection in the target area is realized without accurately distinguishing the obstacle categories, which reduces the spatial and temporal complexity of the road environment perception. The method of this paper is compared and analyzed with the obstacle detection methods based on VIDAR (vision-IMU based detection and range method), VIDAR + MSER, and YOLOv8s. The experimental results show that the method in this paper has high detection accuracy and verifies the feasibility of obstacle detection in road environments where unknown obstacles exist.