Small Object Detection Algorithm for UAV Aerial Images in Complex Environments

LIU Jie; LIU Shuhao; TIAN Ming; CUI Zhigang

doi:10.11999/JEIT251126

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LIU Jie, LIU Shuhao, TIAN Ming, CUI Zhigang. Small Object Detection Algorithm for UAV Aerial Images in Complex Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251126

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LIU Jie, LIU Shuhao, TIAN Ming, CUI Zhigang. Small Object Detection Algorithm for UAV Aerial Images in Complex Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251126

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Small Object Detection Algorithm for UAV Aerial Images in Complex Environments

doi: 10.11999/JEIT251126 cstr: 32379.14.JEIT251126

1.
School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
2.
China Telecom Heilongjiang Branch, Harbin 150040, China
3.
Heilongjiang Provincial Highway Construction Center, Harbin 150001, China

Funds: Supported by Natural Science Foundation of Heilongjiang Province (No. LH2023E086), Science and Technology Project of Heilongjiang Provincial Communications Department (No. HJK2024B002)

Accepted Date: 2026-01-22
Rev Recd Date: 2026-01-22

Available Online: 2026-02-11

Abstract

Abstract

Objective Small object detection plays a critical role in practical applications such as UAV (Unmanned Aerial Vehicle) inspection and intelligent transportation systems, where precise perception of diminutive targets is essential for operational reliability and safety. It enables the automated identification and tracking of challenging targets. However, the limited pixel size of small objects, coupled with their tendency to be obscured or integrated with complex backgrounds, results in strong background noise, leading to poor performance and elevated false-negative rates in existing detection models. To address this issue and achieve high-performance, high-precision detection of small objects in complex environments, this study proposes HAR-DETR, an enhancement over the RT-DETR baseline model, aimed at improving the detection accuracy for small objects. Methods HAR-DETR is proposed for small object detection in aerial images, incorporating three key improvements: Aggregated Attention, RFF-FPN (Recalibrated Feature Fusion Network-FPN), and a high-resolution detection branch. In the backbone network, Aggregated Attention enhances the model's ability to focus on relevant features of small objects. By expanding the receptive field, the model captures more detailed edge and texture information, thereby enabling more effective extraction of multi-scale features of the targets. During the feature fusion phase, RFF-FPN selectively integrates high-level and low-level features, allowing the network to retain critical spatial information and context. This facilitates the refinement of the edges and contours of small objects, improving the accuracy of localization and recognition, especially when object details may be obscured by background clutter or varying lighting conditions. The high-resolution detection head places greater emphasis on the edge features of small objects, providing enhanced small object perception capabilities, and further improving the model's robustness and precision. Results and Discussions A comparative analysis is conducted with several widely used object detection models, including YOLOv5, YOLOv8, YOLOv10 and so on, to evaluate the performance of the model in small object detection using precision, recall, and mAP metrics. Experimental results show that the HAR-DETR model outperforms other comparative models in terms of precision, recall, and mAP on the VisDrone2019 dataset (Table 1). The mAP50 and mAP50-95 are improved by 3.8% and 3.2%, respectively, compared to the baseline model (Table 2). This demonstrates that the HAR-DETR model offers superior performance in detecting small objects in aerial images under complex environments. Heatmaps generated using GradCAM are utilized for comparative analysis of the proposed improvements, showing better detection results for all improvements compared to the baseline model (Fig. 6). In the generalization performance experiment, the VisDrone2019 validation set and RSOD dataset are used under identical training conditions. The experimental results indicate that HAR-DETR exhibits strong generalization ability across heterogeneous tasks (Tables 3 and 4). Conclusions This paper addresses the issues of false positives and false negatives in small object detection within aerial images captured in complex environments by utilizing the HAR-DETR model. Aggregated Attention is introduced in the backbone feature extraction phase to expand the receptive field and enhance global feature extraction capabilities. In the feature fusion phase, the RFF-FPN structure is proposed to enrich the feature representations. Additionally, a high-resolution detection head is introduced to make the model more sensitive to the edge textures of small objects. The model is evaluated using the Visdrone2019 and RSOD datasets, and the results demonstrate the following: (1) The proposed method improves the small object detection metrics, mAP50 and mAP50-95, by 3.8% and 3.2%, respectively, compared to the baseline model, achieving 51.2% and 32.1%, and mitigating the issues of false negatives and false positives; (2) In comparison with other mainstream object detection models, HAR-DETR exhibits the best performance in small object detection, thereby fully validating the effectiveness of the model; (3) The HAR-DETR model achieves high accuracy in cross-dataset training, demonstrating its excellent generalization performance. These results indicate that HAR-DETR possesses stronger semantic expression and spatial awareness capabilities, making it adaptable to various aerial perspectives and target distribution patterns, thus providing a more versatile solution for UAV visual perception systems in complex environments.
- Small object detection,
- RT-DETR,
- Feature fusion,
- Aerial images

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References(22)

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