Abstract
Object detection has always been one of the hot tasks in the computer vision community, whose goal is to locate the instances from the image and predict instances’ category. In recent years, with the development of deep learning technology, both the accuracy and speed of object detection have made great progress. However, limited by the low resolution and little feature information of the small objects, detecting the small object is still facing many difficulties and attracting more and more researchers’ attention. In this paper, we first introduce the mainstream object detection algorithms, and then detail the development of small object detection algorithms from the perspective of the data enhancement, context learning, adversarial learning, feature fusion, and other aspects. Also, we analyze the performance of these representative algorithms on the common datasets. Finally, we summarize the existing problems and prospect the possible future development direction in the small object detection research field.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.