In this paper, we present a novel methodology based on machine learning for identifying the
most appropriate from a set of available state-of-the-art object detectors for a given …

Object detection is an essential computer vision task that possesses extensive application
prospects in on-road applications. Copious novel methods have been proposed in this …

Object detection from aerial and satellite remote sensing images has been an active
research topic over the past decade. Thanks to the increase in computational resources and …

The field of computer vision, particularly object detection, has undergone significant
changes. Most cutting-edge object detectors can accurately detect medium and large …

Object detection models are usually trained and evaluated on highly complicated,
challenging academic datasets, which results in deep networks requiring lots of …

Object detection, a crucial aspect of computer vision, has seen significant advancements in
accuracy and robustness. Despite these advancements, practical applications still face …

Small object detection is an open challenge due to its limited resolution and information.
Existing object detection pipelines can't meet the requirement of accuracy for small objects …

The detection of multi-class small objects poses a significant challenge in the field of
computer vision. While the original YOLOv5 algorithm is more suited for detecting full-scale …

There are mainly two types of state-of-the-art object detectors. On one hand, we have two-
stage detectors, such as Faster R-CNN (Region-based Convolutional Neural Networks) or …

As autonomous vehicles and autonomous racing rise in popularity, so does the need for
faster and more accurate detectors. While our naked eyes are able to extract contextual …