SM-YOLO:实时烟雾探测模型

Zhen Yang, Han Yu, Lei Xu, Fan Yang, Zhijian Yin
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引用次数: 0

摘要

为了解决缺乏最新的烟雾探测数据集的问题,我们编译并标记了各种烟雾探测数据集,称为SM-dataset。该数据集共包含11596张来自自然场景的烟雾图像。同时,我们推出了性能更好的新版YOLO,我们称之为SM-YOLO。SM-YOLO在YOLOv5m原有模型的基础上,将原有的三个输出减少到两个,简化了原有网络结构,提高了原有网络的损耗。与YOLOv5m相比,SM-YOLO只有75%的可训练参数,但提高了mAP@.并将单帧的推理时间从7.3 ms减少到6.6 ms,有效地提高了烟雾检测的速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SM-YOLO: A Model for Real-Time Smoke Detection
To address the lack of up-to-date smoke detection datasets, we have compiled and labeled a variety smoke detection dataset called SM-dataset. This dataset contains a total of 11596 smoke images from natural scenes. Meanwhile, we introduce a new version of YOLO with better performance, which we call SM-YOLO. SM-YOLO builds on the original model of YOLOv5m, reduces the original three outputs to two, streamlines the original network structure and improves the loss of the original network. Compared with YOLOv5m, SM-YOLO has only 75% of the trainable parameters, but improves mAP@.5 by relative 2%, and reduces the inference time of a single frame from 7.3 ms to 6.6 ms, which effectively improves the speed of smoke detection.
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