DALDet: Depth-Aware Learning Based Object Detection for Autonomous Driving

Abstract

3D object detection achieves good detection performance in autonomous driving. However, it requires substantial computational resources, which prevents its practical application. 2D object detection has less computational burden but lacks spatial and geometric information embedded in depth. Therefore, we present DALDet, an efficient depth-aware learning based 2D detector, achieving high-performance object detection for autonomous driving. We design an efficient one-stage detection framework and seamlessly integrate depth cues into convolutional neural network by introducing depth-aware convolution and depth-aware average pooling, which effectively improve the detector's ability to perceive 3D space. Moreover, we propose a depth-guided loss function for training DALDet, which effectively improves the localization ability of the detector. Due to the use of depth map, DALDet can also output the distance of the object, which is of great importance for driving applications such as obstacle avoidance. Extensive experiments demonstrate the superiority and efficiency of DALDet. In particular, our DALDet ranks 1st on both KITTI Car and Cyclist 2D detection test leaderboards among all 2D detectors with high efficiency as well as yielding competitive performance among many leading 3D detectors. Code will be available at https://github.com/hukefy/DALDet.