Towards a fast and accurate road object detection algorithm based on convolutional neural networks

Abstract

Existing algorithms for road object detection currently exhibit three major drawbacks: slow detection speed, difficulty recognizing small objects, and low detection precision. To address these problems, we propose an algorithm called fast and accurate road object detection (FAROD) based on the faster region-based convolutional neural networks (R-CNN). The FAROD has two parts: the accurate object proposal network (AOPN) and object attribute learning network (OALN). The AOPN accurately generates road object-like regions in real time, and the OALN detects the corresponding classifications and locations of road object-like regions. To promote greater accuracy for small object detection, we introduce a deconvolution structure and loss functions for the AOPN and OALN. We improve computation speed by alternately optimizing and jointly training the networks. Our experimental results show that FAROD offers significant improvement over existing road object detection algorithms in dealing with difficult images, especially road images within small objects. The test results of the mean average precision improved by 4.5%, and the detection time reduced by one-third than that of the state-of-the-art object detection algorithm faster R-CNN.