부산대학교 도서관

Object detection and classification are key processes in advanced driver-assistance systems. The existing object detection and classification methods are effective in normal daylight conditions. However, the performance of these methods deteriorates in adverse driving conditions, such as those involving low light, illumination changes, and nighttime conditions. To overcome these limitations, several feature-based algorithms have been developed that introduce local features, such as local binary pattern, local tetra pattern, and local density encoding, for adverse driving conditions. However, these local patterns cannot effectively address the noise in real driving conditions because the relationship between the neighboring pixels cannot be comprehensively encoded. To solve these problems, this study developed a robust feature-based method by introducing a triangular-pattern-based sigmoid function to effectively encode and establish the robust feature of neighboring pixels in the local region. The performance of the proposed pattern is evaluated by integrating it into state-of-the-art object detection algorithms. The proposed method significantly increases the vehicle detection ratio of YOLOv5s by 11.7% for an intersection over a union of 0.5 in difficult driving conditions for the CCD dataset. Moreover, the detection ratios of the proposed method are comparable to those of other state-of-the-art object detection methods such as Retina, Faster RCNN, and Deformable DETR over various datasets such as KITTI, COCO, HCI, and CCD. Additionally, the proposed algorithm is implemented on a Raspberry Pi-based autonomous car system to evaluate its performance during real driving conditions. Our proposed method supports robust input feature extraction and can thus be used to enhance the performance of the existing obstacle detection and classification systems.