Deep learning models are widely being employed for object detection due to their high
performance. However, the majority of applications that require object detection are …

This study concentrates on deep learning-based lightweight object detection models on
edge devices. Designing such lightweight object recognition models is more difficult than …

Despite significant advances and innovations in deep network-based vehicle detection
methods, finding a balance between detector accuracy and speed remains a significant …

The development of object detection has led to huge improvements in human interaction
systems. Object detection is a challenging task because it involves many parameters …

Tunnel construction sites pose a significant safety risk to workers due to the low-light
conditions that can affect visibility and lead to accidents. Therefore, identifying personal …

Water conservancy personnel usually need to know the water level by water gauge images
in real-time and with an expected accuracy. However, accurately recognizing the water level …

Recently, removing rain streaks from a single image has attracted a lot of attention because
rain streaks can severely degrade the perceptual quality of the image and cause many …

The 3D visual perception for vehicles with the surround-view fisheye camera system is a
critical and challenging task for low-cost urban autonomous driving. While existing …

Adversarial training suffers from poor effectiveness due to the challenging optimisation of
loss with hard labels. To address this issue, adversarial distillation has emerged as a …

Object detection algorithm has high recognition accuracy and fast detection speed, but there
are also obvious problems. The positioning is inaccurate because the network uses an end …