Toward Structural Learning and Enhanced YOLOv4 Network for Object Detection in Optical Remote Sensing Images. Issue 6 (9th March 2022)

• Record Type: Journal Article
• Authors: Wang, Kun; Liu, Maozhen
• Is Part Of: Advanced theory and simulations. Volume 5:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-09
• Subjects: convolutional neural networks -- multi‐scale objects -- object detection -- optical remote sensing images -- structural learning; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202200002
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: With the maturity of technological tools such as satellites and airplanes, object detection in optical remote sensing images have been widely used in military and civilian fields. Due to the interference of extensive multi‐scale targets and complex backgrounds, the existing algorithms are still limited, especially for small‐scale targets. To solve this problem, a high‐precision remote sensing detection method based on the advanced YOLOv4 framework is proposed. First, a clustering algorithm that combines knowledge of object scales to generate a priori anchor boxes with a higher matching degree is proposed. The feature extension module is designed to expand the receptive field of the backbone network and capture important contextual information. After that, coordinate attention (CA) is introduced to suppress the extra noise under multi‐scale fusion that is rarely noticed in previous work. In addition, a convolutional idea of self‐learning structure has also been novelly proposed and combined with a high‐resolution network to efficiently enhance the performance of the network under limited computing resources. Finally, detailed experiments are performed on the HRRSD dataset. The experimental results show that the network has a more satisfactory superiority, and it is conducive to the advancement of optical remote sensing technology. Abstract : Due to the interference of extensive multi‐scale targets and complex backgrounds, the existing algorithms are still limited, especially for small‐scale targets. To solve this problem, a high‐precision remote sensing detection method based on the advanced YOLOv4 framework including novel clustering methods, feature extension module, multi‐scale fusion, and structure learning high‐resolution network is proposed.