Robust Vehicle Detection Under Adverse Weather Conditions Using Auto-encoder Feature

Abstract

Existing deep learning-based obstacle detection systems are often designed and implemented based on raw input feature. These systems obtain high accuracy under normal driving conditions. But they fail to operate under difficult driving conditions, which are different from their training. Recently, an unsupervised auto-encoder has been successfully applied to produce robust input features for a stereo matching system under difficult driving conditions. Therefore, this paper investigates an auto-encoder feature to improve the performance of existing vehicle detections under adverse weather conditions. Experimental results show that the proposed method obtained better result than existing state-of-the-art object detection methods in term of accuracy. Index Terms-Vehicle detection, auto-encoder, deep learning, and local binary pattern. Vinh Dinh Nguyen received the B.Sc. (magna cum laude

Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network

“…After that, a Region Proposed Network (RPN) is placed which slides over the feature maps and makes bounding boxes containing vehicles. At the final stage a R-CNN based detection network is used to assign class label to each ROI (Region of Interest) [113] supplies input images to an auto encoder layer before passing it to the deep learning framework. The AE neural network rather works as a pre-processing step and extracts more fine-grained robust features.…”