Yao, G.; Zhu, S.; Zhang, L.; Qi, M. HP-YOLOv8: High-Precision Small Object Detection Algorithm for Remote Sensing Images. Sensors2024, 24, 4858.
Yao, G.; Zhu, S.; Zhang, L.; Qi, M. HP-YOLOv8: High-Precision Small Object Detection Algorithm for Remote Sensing Images. Sensors 2024, 24, 4858.
Yao, G.; Zhu, S.; Zhang, L.; Qi, M. HP-YOLOv8: High-Precision Small Object Detection Algorithm for Remote Sensing Images. Sensors2024, 24, 4858.
Yao, G.; Zhu, S.; Zhang, L.; Qi, M. HP-YOLOv8: High-Precision Small Object Detection Algorithm for Remote Sensing Images. Sensors 2024, 24, 4858.
Abstract
YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm
optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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