L4Net: An anchor‐free generic object detector with attention mechanism for autonomous driving

Abstract

Generic object detection is a crucial task for autonomous driving. To devise a safe and efficient object detector, the following aspects are required to be considered: high accuracy, real‐time inference speed and small model size. Herein, a simple yet effective anchor‐free object detector named L4Net is proposed, which incorporates a keypoint detection backbone and a co‐attention scheme into a unified framework, and achieves lower computation cost with higher detection accuracy than prior art across a wide spectrum of resource constrains. Specifically, the backbone utilizes Multi‐scale Receptive‐fields Enhancement module (MRE) to capture context‐wise information, where the features of object scale and shape invariance are simultaneously considered. The co‐attention scheme integrates the strength of both Class‐agnostic Attention (CA) and Semantic Attention (SA), and explores the valuable features from low‐level to high‐level to generate more accurate prediction boxes. Compared with previous feature fusion strategy, multi‐scale features are selectively integrated by fully exploiting the different characteristics of low‐level and high‐level features, which leads to a small model size and faster inference speed. Extensive experiments on four well‐known datasets demonstrate the effectiveness of our method. For instance, L4Net achieves 71.68% mAP on KITTI test set, with 13.7 M model size at the speed of 149 FPS on NVIDIA TX and 30.7 FPS on Qualcomm‐based device, respectively, which is 4x smaller and 2x faster than baseline model.

Clean, performance‐robust, and performance‐sensitive historical information based adversarial self‐distillation

“…However, the susceptibility of DNNs to adversarial attacks involving imperceptible input perturbations, as identified by Szegdy et al [7] poses a significant challenge. These perturbations can lead to erroneous predictions by DNNs, thereby impeding their application in trust-sensitive domains, such as autonomous driving [8,9], facial authentication [2,10], and healthcare [11,12].…”

An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks

“…Object detection is the first and most essential step in the field of computer vision [14][15][16] and brings a leap to many other fields, such as autonomous driving in smart transportation, intelligent security, and remote sensing [17][18][19][20][21][22][23]. The combined approach of HOG and SVM has poor results in object detection due to the shortcomings of the feature extractor.…”