A lightweight YOLOv5 for real-time dangerous weapons detection

Aicha Khalfaoui, Abdelmajid Badri, Ilham El Mourabit
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Abstract

Deep neural networks are currently employed to detect weapons, and although these techniques provide a high level of accuracy, it still suffers from large weight parameters and a slow inference speed. When it comes to real-world applications, such as weapon detection, these methods are often not suitable for deployment on embedded devices. Because of the huge number of parameters and poor efficiency. The most recent object detection technique, which belongs to the YOLOv5 class, is commonly used for detecting weapons. However, it faces some difficulties such as high computational parameters and an unfavorable detection rate. to solve these shortcomings. an enhanced lightweight Yolov5s approach is suggested. Which consists of a combination of YOLOv5 and GhostNet modules. To evaluate the efficacy of the suggested technique, a set of experiments was performed on the Sohas weapon dataset., which is commonly used as a reference dataset in the field. Compared to the original YOLOv5, the results indicate a slight increase in the proposed model's mean Average Precision (mAP). Furthermore, there has been a reduction of 2.7 in GFLOPs and weights, and the number of model parameters has decreased by 1.42.
用于实时探测危险武器的轻型 YOLOv5
目前,深度神经网络被用于检测武器,虽然这些技术具有很高的准确性,但仍存在权重参数大、推理速度慢的问题。在实际应用中,如武器检测,这些方法往往不适合部署在嵌入式设备上。因为参数数量庞大,效率低下。最新的物体检测技术属于 YOLOv5 类,常用于检测武器。为了解决这些问题,我们提出了一种增强型轻量级 Yolov5s 方法。该方法由 YOLOv5 和 GhostNet 模块组合而成。为了评估所建议技术的有效性,我们在 Sohas 武器数据集上进行了一系列实验,该数据集通常被用作该领域的参考数据集。结果表明,与最初的 YOLOv5 相比,建议模型的平均精度(mAP)略有提高。此外,GFLOP 和权重减少了 2.7,模型参数数量减少了 1.42。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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