Guangzhen Yao

In recent years, hybrid convolutional neural networks (CNNs) and Transformer-based object detection technologies have achieved remarkable success. In the field of remote sensing image detection, since remote sensing systems rely on large-scale deployment of edge devices, detection models need to be lightweight with low parameter complexity to adapt to resource-constrained environments. However, existing lightweight models often struggle with an imbalance in extracting low-frequency global and high-frequency local information. In particular, when processing high-frequency local information (such as edges, textures, and fine structures), these models often lack in-depth analysis, leading to insufficient extraction of local features and reduced detection accuracy. To address the imbalance between low-frequency global information and high-frequency local information in lightweight remote sensing models, we propose an efficient and lightweight hybrid network detection framework, which mainly consists of the Global-Local Balance (GLB) module and the Detail-Aware Feature Fusion (DAFF) module. The GLB module adopts dynamic weight adjustment and context-aware mechanisms to effectively aggregate high-frequency local information in the image. The DAFF module further enhances feature fusion and detail refinement, improving the model’s performance and generalization ability. Experimental results on remote sensing datasets, including RSOD, NWPU VHR-10, and LEVIR, demonstrate that our proposed method achieves a well-balanced trade-off between model size and detection accuracy, reaching state-of-the-art performance.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small objects 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 objects. Finally, we propose the Shape 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 conducted 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.

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.

Guangzhen Yao, Yuehan Wang, Hui Xu, Long Zhang, MiaoQI MiaoQI. Proceedings of the 28th Conference on Computational Natural Language Learning. 2024.