UAV image object detection based on improved YOLOv8

Wei Zhang; Jiaxin Wang; Dan Wang; Xiangwei Kong

doi:10.1117/12.3038589

23 August 2024 UAV image object detection based on improved YOLOv8

Wei Zhang, Jiaxin Wang, Dan Wang, Xiangwei Kong

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132501A (2024) https://doi.org/10.1117/12.3038589
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In a variety of UAV application scenarios, such as agricultural monitoring, urban safety and traffic management, high-precision image object detection is crucial for real-time analysis and decision-making. Traditional object detection algorithms are often ineffective in dealing with small objects in complex environments. To this end, this paper proposes an improved YOLOv8 model that integrates a NAM (Normalization-based Attention Module) attention mechanism and a Bidirectional Feature Pyramid Network (BiFPN) specifically to optimize the object detection performance of UAV images. The NAM module enhances the model's spatial attention capability by utilizing a normalization process that This enables the model to adaptively emphasize important feature regions in the image, which significantly improves the recognition accuracy of small-scale objects without adding too much computational burden. BiFPN further optimizes the flow of information between different scales, which improves the efficiency of feature utilization and the overall performance of detection. Extensive experiments on the VisDrone2019 dataset show that the improved YOLOv8 model improves the mAP metrics by 11.3% compared to the original model, and performs especially well in scenes with complex backgrounds and the presence of multi-scale objects. This study not only validates the potential application of NAM in UAV vision tasks, but also provides a new technical path for the implementation of deep learning models in real monitoring systems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei Zhang, Jiaxin Wang, Dan Wang, and Xiangwei Kong "UAV image object detection based on improved YOLOv8", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132501A (23 August 2024); https://doi.org/10.1117/12.3038589

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KEYWORDS

Object detection

Feature fusion

Unmanned aerial vehicles

Visual process modeling

Performance modeling

Image processing

Target detection

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