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Detection of abandoned objects based on Yolov9 and background differencing

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Abstract

Abandoned objects on highways can cause serious traffic accidents. Abandoned objects detection algorithms are of great significance in reducing traffic accidents and improving the efficiency of highway management. Existing traditional image processing detection algorithms are difficult to detect small targets in the complex and changing highway environment. And the current deep learning methods are limited by the number of samples and the influence of vehicle occlusion resulting in lower detection accuracy. Aiming at the above problems, a abandoned object detection algorithm for highway is designed by combining the background modeling algorithm MOG2 and the YOLOv9 detector. Utilizing the difference of the two algorithms on the recognition mechanism of abandoned objects, the results of the YOLOv9 detector are used as a guide to make a comprehensive judgment on the low confidence targets combined with the iterative difference results of MOG2. The actual test results on highways show that the abandoned objects detection algorithm based on YOLOv9 and MOG2 has an improved precision and recall rate for abandoned objects detection compared to a single detection algorithm, with an average precision of 86.7\(\%\) and a recall rate of 89.6\(\%\). The algorithm effectively complements the existing abandoned objects detection algorithms and has engineering practical value.

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Data availability

The data utilized in this study are not publicly available due to restrictions imposed by the third-party organization from which they were obtained.

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Funding

This thesis is supported by the GF Technology and Innovation Special Zone Program.

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Huajun Song Conceptualization, Methodology, Investigation, Writing-Original Draft Jinbo Wang Data Curation, Writing-Original Draft, Formal Analysis Yunze Zhang Resources, Formal Analysis,Writing-Original Draft

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Correspondence to Jinbo Wang.

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Song, H., Wang, J. & Zhang, Y. Detection of abandoned objects based on Yolov9 and background differencing. SIViP 19, 54 (2025). https://doi.org/10.1007/s11760-024-03609-z

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