Enhancing UAV Aerial Image Analysis: Integrating Advanced SAHI Techniques With Real-Time Detection Models on the VisDrone Dataset

Muzammul, M.; Algarni, Abdulmohsen; Ghadi, Yazeed Yasin; Assam, Muhammad

doi:10.1109/access.2024.3363413

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…Comparing the improved YOLOv5 with state-of-the-art aerial image detection algorithms, such as the recent research from Zhejiang University—combining the real-time detection and recognition (RT-DETR-X) model with the SAHI (slice-assisted hyper-inference) method [ 44 ] for detection using the VisDrone-DET dataset—the improved YOLOv5 slightly lags behind the RT-DETR-X model, but YOLOv5 has superior processing speed and a higher FPS rate, which is crucial for real-time surveillance applications.…”

Section: Conclusion and Future Workmentioning

confidence: 99%

An Aerial Image Detection Algorithm Based on Improved YOLOv5

Shan,

Yang,

Wang

et al. 2024

Sensors

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To enhance aerial image detection in complex environments characterized by multiple small targets and mutual occlusion, we propose an aerial target detection algorithm based on an improved version of YOLOv5 in this paper. Firstly, we employ an improved Mosaic algorithm to address redundant boundaries arising from varying image scales and to augment the training sample size, thereby enhancing detection accuracy. Secondly, we integrate the constructed hybrid attention module into the backbone network to enhance the model’s capability in extracting pertinent feature information. Subsequently, we incorporate feature fusion layer 7 and P2 fusion into the neck network, leading to a notable enhancement in the model’s capability to detect small targets. Finally, we replace the original PAN + FPN network structure with the optimized BiFPN (Bidirectional Feature Pyramid Network) to enable the model to preserve deeper semantic information, thereby enhancing detection capabilities for dense objects. Experimental results indicate a substantial improvement in both the detection accuracy and speed of the enhanced algorithm compared to its original version. It is noteworthy that the enhanced algorithm exhibits a markedly improved detection performance for aerial images, particularly under real-time conditions.

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Section: Conclusion and Future Workmentioning

confidence: 99%

An Aerial Image Detection Algorithm Based on Improved YOLOv5

Shan,

Yang,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

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Research on multi-view collaborative detection system for UAV swarms based on Pix2Pix framework and BAM attention mechanism

Ding,

Cao,

Zhang

et al. 2024

Defence Technology

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Drone-TOOD: A Lightweight Task-Aligned Object Detection Algorithm for Vehicle Detection in UAV Images

Ou,

Dong,

Liu

et al. 2024

IEEE Access

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Vehicle object detection using UAV images is a crucial undertaking in urban traffic management and the advancement of autonomous driving technologies. Conventional networks fail to achieve accuary in detecting vehicle objects from a drone's perspective due to the significant variations in the size of the target items, unequal distribution of their positions in the image, and image degradation induced by the drone's movement.In order to surmount this challenge, this research suggests an enhanced TOOD object detection model named Drone-TOOD. The first proposal is to create a lightweight network skeleton called CSPRegNet by merging CSPblock with Regblock. Secondly, we incorporate Regblock into CSPPAFPN to enhance CSPRegPAFPN and incorporate EVCblock at the upsampling location of deep features to capture corner area details and minimize the degradation of feature information. In addition, a efficient task decomposition attention module is also proposed to enhance the interaction ability of positioning and classification tasks. This task decomposition module can highlight the characteristics of a specific task while retaining the characteristics of another task, thereby improving detection capabilities. Experiments conducted on the Drone Vision Challenge Benchmark (VisDrone) demonstrate that the enhanced model can obtain superior performance compared to TOOD. The average precision (mAP) achieved by our approach is 64%, surpassing TOOD by 7.9%. The frames per second (FPS) stayed constant at 27.2. Drone-TOOD demonstrates superior performance compared to other lightweight models on the VisDrone-2021 dataset. In order to demonstrate the robustness of our approach, we additionally performed ablation experiments and conducted tests on the UAV Detection and Tracking Dataset (UAVDT), resulting in an achieved mean average precision (mAP) of 64.6%. Furthermore, Drone-TOOD possesses a total of parameter approximately 21.7 M.

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Enhancing UAV Aerial Image Analysis: Integrating Advanced SAHI Techniques With Real-Time Detection Models on the VisDrone Dataset

Cited by 7 publications

References 31 publications

An Aerial Image Detection Algorithm Based on Improved YOLOv5

An Aerial Image Detection Algorithm Based on Improved YOLOv5

Research on multi-view collaborative detection system for UAV swarms based on Pix2Pix framework and BAM attention mechanism

Drone-TOOD: A Lightweight Task-Aligned Object Detection Algorithm for Vehicle Detection in UAV Images

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