With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

Aiming at the problems of high missed detection rates of the YOLOv7 algorithm for vehicle
detection on urban roads, weak perception of small targets in perspective, and insufficient …

Computer vision‐based techniques were developed to overcome the limitations of visual
inspection by trained human resources and to detect structural damage in images remotely …

Intelligent vehicle detection and counting are becoming increasingly important in the field of
highway management. However, due to the different sizes of vehicles, their detection …

Automatic moving vehicle detection plays a crucial and challenging role in performing
intelligent traffic surveillance. Numerous research projects aiming to perform proper …

Detecting buried objects from ground penetrating radar (GPR) profiles often requires manual
interaction and plenty of time. This paper presents an automatic scheme for buried objects …

An intelligent lithology identification method is proposed based on the deep learning of rock
images. The lithology information and position information in rock images can be predicted …

The recent boom of autonomous driving nowadays has made object detection in traffic
scenes a hot topic of research. Designed to classify and locate instances in the image, this is …

Urban areas are facing challenges in waste management systems due to the rapid growth of
population in cities, causing huge amount of waste generation. As traditional waste …