Recent advancements in artificial intelligence have led to significant improvements in object
detection. Researchers have focused on enhancing the performance of object detection in …

Convolutional neural networks (CNNs) have made remarkable progress in detecting vehicle
objects in normal weather conditions. However, the performance of these networks …

Vehicle detection based on deep learning achieves excellent results in normal
environments, but it is still challenging to detect objects in low-quality picture obtained in …

Large cities' expanding populations are causing traffic congestion. The maintenance of the
city's road network necessitates ongoing monitoring, growth, and modernization. An …

Recently, Computer vision has been one of the widespread forms of object detection. It is
challenging to detect an object. When the dataset is trained by a luminous during bad …

Numerous vehicle detection methods have been proposed to obtain trustworthy traffic data
for the development of intelligent traffic systems. Most of these methods perform sufficiently …

Driven by deep learning, object detection methods have made great progress in recent
years. However, under adverse weather conditions, such as foggy, dust, rain, snow and …

In a bad weather environment, the existing road object–detection algorithms using deep
learning seldom properly detect road objects in fog, rain, and/or snow. This is due to the lack …

For autonomous driving, perception is a primary and essential element that fundamentally
deals with the insight into the ego vehicle's environment through sensors. Perception is …

Object detection is crucial in diverse autonomous systems like surveillance, autonomous
driving, and driver assistance, ensuring safety by recognizing pedestrians, vehicles, traffic …