Anchor-Free Object Detection with Scale-Aware Networks for Autonomous Driving

Current anchor-free object detectors do not rely on anchors and obtain comparable accuracy with anchor-based detectors. However, anchor-free object detectors that adopt a single-level feature map and lack a feature pyramid network (FPN) prior information about an object’s scale; thus, they insufficiently adapt to large object scale variation, especially for autonomous driving in complex road scenes. To address this problem, we propose a divide-and-conquer solution and attempt to introduce some prior information about object scale variation into the model when maintaining a streamlined network structure. Specifically, for small-scale objects, we add some dense layer jump connections between the shallow high-resolution feature layers and the deep high-semantic feature layers. For large-scale objects, dilated convolution is used as an ingredient to cover the features of large-scale objects. Based on this, a scale adaptation module is proposed. In this module, different dilated convolution expansion rates are utilized to change the network’s receptive field size, which can adapt to changes from small-scale to large-scale. The experimental results show that the proposed model has better detection performance with different object scales than existing detectors.