In rapid urban development,outdoor parking lots have become essential components of urban transportation systems.However,the increasing number of parking lots is accompanied by a rising risk of vehicle fires,posing a ...In rapid urban development,outdoor parking lots have become essential components of urban transportation systems.However,the increasing number of parking lots is accompanied by a rising risk of vehicle fires,posing a serious challenge to public safety.As a result,there is a critical need for fire warning systems tailored to outdoor parking lots.Traditional smoke detection methods,however,struggle with the complex outdoor environment,where smoke characteristics often blend into the background,resulting in low detection efficiency and accuracy.To address these issues,this paper introduces a novel model named Dynamic Contextual Transformer YOLO(DCT-YOLO),an advanced smoke detection method specifically designed for outdoor parking lots.We introduce an innovative Dynamic Channel-Spatial Attention(DCSA)mechanism to improve the model’s focus on smoke features,thus improving detection accuracy.Additionally,we incorporate Contextual Transformer Networks(CoTNet)to better adapt to the irregularity of smoke patterns,further enhancing the accuracy of smoke region detection in complex environments.Moreover,we developed a new dataset that includes a wide range of smoke and fire scenarios,improving the model’s generalization capability.All baseline models were trained and evaluated on the same dataset to ensure a fair and consistent comparison.The experimental results on this dataset demonstrate that the proposed algorithm yields a mAP@0.5 of 85.1%and a mAP@0.5:0.95 of 55.7%,representing improvements of 15.0%and 14.9%,respectively,over the baseline model.These results highlight the effectiveness of the proposed method in accurately detecting smoke in challenging outdoor environments.展开更多
针对森林火灾发生初期烟雾检测模型存在精度差、错检率高以及缺乏烟雾源检测的问题,提出一种基于改进YOLOv5的森林烟雾及烟雾源检测算法。该算法在特征融合区域加入CA注意力机制,以增强模型对输入数据的空间结构理解;通过在骨干网络加...针对森林火灾发生初期烟雾检测模型存在精度差、错检率高以及缺乏烟雾源检测的问题,提出一种基于改进YOLOv5的森林烟雾及烟雾源检测算法。该算法在特征融合区域加入CA注意力机制,以增强模型对输入数据的空间结构理解;通过在骨干网络加入上下文转换器网络(contextual transformer networks,CoTNet),提高网络相邻键的上下文感知能力;后更换损失函数为Shape-IoU提高算法定位精度。结果表明,使用自建数据集训练改进的YOLOv5模型,森林烟雾及烟雾源检测精度达到95.1%。与YOLOv7、YOLOv7-tiny模型相比,改进的YOLOv5模型的准确率、召回率、平均精度均值(mean average precision,mAP)分别提高4.4%~22.7%、9.2%~26.3%、4.6%~26.9%。展开更多
基金supported by the Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘In rapid urban development,outdoor parking lots have become essential components of urban transportation systems.However,the increasing number of parking lots is accompanied by a rising risk of vehicle fires,posing a serious challenge to public safety.As a result,there is a critical need for fire warning systems tailored to outdoor parking lots.Traditional smoke detection methods,however,struggle with the complex outdoor environment,where smoke characteristics often blend into the background,resulting in low detection efficiency and accuracy.To address these issues,this paper introduces a novel model named Dynamic Contextual Transformer YOLO(DCT-YOLO),an advanced smoke detection method specifically designed for outdoor parking lots.We introduce an innovative Dynamic Channel-Spatial Attention(DCSA)mechanism to improve the model’s focus on smoke features,thus improving detection accuracy.Additionally,we incorporate Contextual Transformer Networks(CoTNet)to better adapt to the irregularity of smoke patterns,further enhancing the accuracy of smoke region detection in complex environments.Moreover,we developed a new dataset that includes a wide range of smoke and fire scenarios,improving the model’s generalization capability.All baseline models were trained and evaluated on the same dataset to ensure a fair and consistent comparison.The experimental results on this dataset demonstrate that the proposed algorithm yields a mAP@0.5 of 85.1%and a mAP@0.5:0.95 of 55.7%,representing improvements of 15.0%and 14.9%,respectively,over the baseline model.These results highlight the effectiveness of the proposed method in accurately detecting smoke in challenging outdoor environments.
文摘针对森林火灾发生初期烟雾检测模型存在精度差、错检率高以及缺乏烟雾源检测的问题,提出一种基于改进YOLOv5的森林烟雾及烟雾源检测算法。该算法在特征融合区域加入CA注意力机制,以增强模型对输入数据的空间结构理解;通过在骨干网络加入上下文转换器网络(contextual transformer networks,CoTNet),提高网络相邻键的上下文感知能力;后更换损失函数为Shape-IoU提高算法定位精度。结果表明,使用自建数据集训练改进的YOLOv5模型,森林烟雾及烟雾源检测精度达到95.1%。与YOLOv7、YOLOv7-tiny模型相比,改进的YOLOv5模型的准确率、召回率、平均精度均值(mean average precision,mAP)分别提高4.4%~22.7%、9.2%~26.3%、4.6%~26.9%。
