Hazardous material vehicles are a non-negligible mobile source of danger in transport and
pose a significant safety risk. At present, the current detection technology is well developed …

Vehicle detection technology is of great significance for realizing automatic monitoring and
AI-assisted driving systems. The state-of-the-art object detection method, namely, a class of …

The YOLOv4 approach has gained significant popularity in industrial object detection due to
its impressive real-time processing speed and relatively favorable accuracy. However, it has …

Object detection in autonomous driving scenarios has become a popular task in recent
years. Due to the high-speed movement of vehicles and the complex changes in the …

Although the YOLOv2 method is extremely fast on object detection, its detection accuracy is
restricted due to the low performance of its backbone network and the under-utilization of …

The perception system in autonomous driving mainly uses object detection algorithms to
obtain the distribution of obstacles for recognition and analysis. Current object detection …

Object detection is an important part of autonomous driving technology. To ensure the safe
running of vehicles at high speed, real‐time and accurate detection of all the objects on the …

Aiming at the low accuracy problem of detecting small and dense objects in urban street
scenes, an improved YOLOv5 object detection algorithm called City-YOLO was proposed …

An algorithm based on improved YOLOv5s is proposed to solve the problems of false and
missing vehicle detections. Firstly, a coordinate-attention (CA) module is added to the …

Rapid and precise detection and classification of vehicles are vital for the intelligent
transportation systems (ITSs). However, due to small gaps between vehicles on the road …