Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering

Abstract

Multi-class and multi-scale object detection for autonomous driving is challenging because of the high variation in object scales and the cluttered background in complex street scenes. Context information and high-resolution features are the keys to achieve a good performance in multi-scale object detection. However, context information is typically unevenly distributed, and the high-resolution feature map also contains distractive low-level features. In this paper, we propose a location-aware deformable convolution and a backward attention filtering to improve the detection performance. The location-aware deformable convolution extracts the unevenly distributed context features by sampling the input from where informative context exists. Different from the original deformable convolution, the proposed method applies an individual convolutional layer on each input sampling grid location to obtain a wide and unique receptive field for a better offset estimation. Meanwhile, the backward attention filtering module filters the high-resolution feature map by highlighting the informative features and suppressing the distractive features using the semantic features from the deep layers. Extensive experiments are conducted on the KITTI object detection and PASCAL VOC 2007 datasets. The proposed method shows an average 6% performance improvement over the Faster R-CNN baseline, and it has the top-3 performance on the KITTI leaderboard with the fastest processing speed.