Modernized YOLOv4 with large kernels and AVOD2K dataset

Shengjie Luo; Zhigang Liu; Yiting Wang; Jialiang Liu

doi:10.1117/12.2660794

2 February 2023 Modernized YOLOv4 with large kernels and AVOD2K dataset

Shengjie Luo, Zhigang Liu, Yiting Wang, Jialiang Liu

Proceedings Volume 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022); 124621R (2023) https://doi.org/10.1117/12.2660794
Event: International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 2022, Xi'an, China

Abstract

We conduct research on a practical task called Armored Vehicle Object Detection (AVOD), which is designed to real-time locate and identify armored vehicles in the armored cluster. Considering the scarcity of available datasets under the background of AVOD, we carefully collect a new dataset named AVOD2K, containing 2,000 pictures which consist of nine types of armored vehicles in various environments. AVOD2K complements the missing armored vehicle dataset, and it could drive special vehicle object detection in complex scenarios. In addition, we use YOLOv4 as the baseline of the task and propose LK-CSPDarkNet to modernize YOLOv4 by combining depthwise separable convolutions and large kernels. As the parameters decreased by 7% and FLOPs a little drop, the modernized YOLOv4 outperforms the baseline in 2.4 AP.

Citation Download Citation

Shengjie Luo, Zhigang Liu, Yiting Wang, and Jialiang Liu "Modernized YOLOv4 with large kernels and AVOD2K dataset", Proc. SPIE 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 124621R (2 February 2023); https://doi.org/10.1117/12.2660794

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KEYWORDS

Convolution

Visual process modeling

Autoregressive models

Sensors

Camouflage

Feature extraction

Network architectures

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