Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

Underwater object detection is one of the most challenging research topics in computer
vision technology. The complex underwater environment makes underwater images suffer …

Bounding box regression (BBR) has been widely used in object detection and instance
segmentation, which is an important step in object localization. However, most of the existing …

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing
industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of …

Underwater images suffer from severe distortion, which degrades the accuracy of object
detection performed in an underwater environment. Existing underwater image …

In object detection, bounding box regression (BBR) is a crucial step that determines the
object localization performance. However, we find that most previous loss functions for BBR …

Traffic signs are one of the most important information that guide cars to travel, and the
detection of traffic signs is an important component of autonomous driving and intelligent …

Abstract We present Sparse R-CNN, a purely sparse method for object detection in images.
Existing works on object detection heavily rely on dense object candidates, such as k anchor …

We show that the YOLOv4 object detection neural network based on the CSP approach,
scales both up and down and is applicable to small and large networks while maintaining …

DETR is a recently proposed Transformer-based method which views object detection as a
set prediction problem and achieves state-of-the-art performance but demands extra-long …