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Engineering Vehicles Detection for Warehouse Surveillance System Based on Modified YOLOv4-Tiny

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Abstract

The engineering vehicle detection is a key issue for the raw material warehouse scenes. Through the engineering vehicle detection, the working conditions of engineering vehicles in the raw material warehouse can be intelligently managed to prevent large-scale smoke pollution and the danger of smoke and dust. In this paper, we propose an intelligent method based on the framework of YOLOv4-Tiny for locating and identifying the engineering vehicles. In our detection task, the monitoring scenes are complex with a lot of interference. And the scope of monitoring is large. In order to solve these challenging problems, we introduce the Split-attention module to the network, which can adaptively extract important information of the image and improve the receptive field of detection. In addition, we introduce the Dynamic ReLU function to the network, which allow the network to adaptively learn more suitable ReLU parameters based on the input. We also collect a large number of images obtained from the front-end cameras and create a self-built dataset of engineering vehicles. In this paper, we test our method on the COCO dataset and the self-built engineering vehicle dataset. Experimental results show that our method proposed in this paper can detect engineering vehicles with higher accuracy and faster speed, which can be used for engineering vehicle detection in the scenes of raw material storage warehouses.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Xuezhi Xiang, Fanda Meng, Ning Lv & Hang Yin

  2. Key Laboratory of Advanced Marine Communication and InformationTechnology, Ministry of Industry and Information Technology, Harbin, China

    Xuezhi Xiang

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  1. Xuezhi Xiang
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  2. Fanda Meng
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  3. Ning Lv
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  4. Hang Yin
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Correspondence to Xuezhi Xiang.

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This work was supported in part by the National Natural Science Foundation of China under Grant 61401113, in part by the Natural Science Foundation of Heilongjiang Province of China under Grant LC201426, in part by the Fundamental Research Funds for the Central Universities of China under Grant 3072020CF0807.

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Xiang, X., Meng, F., Lv, N. et al. Engineering Vehicles Detection for Warehouse Surveillance System Based on Modified YOLOv4-Tiny. Neural Process Lett 55, 2743–2759 (2023). https://doi.org/10.1007/s11063-022-10982-8

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