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SIE-YOLOv5: Improved YOLOv5 for Small Object Detection in Drone-Captured-Scenarios

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Knowledge Science, Engineering and Management (KSEM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14118))

Abstract

With the widespread application of unmanned aerial vehicles (UAVs), object detection from the perspective of UAVs has received increasing attention from scholars. The high image density and small objects in images captured by UAVs pose significant challenges for detection. To address this problem, we propose a detection algorithm for UAV images, called SIE-YOLOv5. Based on YOLOv5, we fully explore the downsampling process and propose the Spatial Information Extraction (SIE) structure to fully utilize the information in feature maps, and better detect small objects in images. Then, we improve the feature pyramid pooling structure and propose a new LeakySPPF module, which achieves faster speed while maintaining comparable performance. To better locate attention regions, we incorporate the attention mechanism CBAM [1] into the model. Finally, we also improve the IoU calculation method of the baseline model by introducing Wise-IoU [2] to address calculation issues. Through extensive experiments, we demonstrate that our proposed SIE-YOLOv5 has better small object detection capabilities in UAV-captured scenes. On the VisDrone2021 dataset, the mAP is improved by 6.6%.

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Correspondence to Zonghui Wen .

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Wen, Z., Su, J., Zhang, Y. (2023). SIE-YOLOv5: Improved YOLOv5 for Small Object Detection in Drone-Captured-Scenarios. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14118. Springer, Cham. https://doi.org/10.1007/978-3-031-40286-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-40286-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40285-2

  • Online ISBN: 978-3-031-40286-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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