海滩垃圾日益增多,流入大海不但会对海洋环境造成污染,而且会影响海洋生物健康。对海滩垃圾进行识别并分类处理具有重要意义。提出基于改进的(you only look once version4,YOLO-v4)目标检测算法的垃圾识别分类方案。通过现场拍摄海滩...海滩垃圾日益增多,流入大海不但会对海洋环境造成污染,而且会影响海洋生物健康。对海滩垃圾进行识别并分类处理具有重要意义。提出基于改进的(you only look once version4,YOLO-v4)目标检测算法的垃圾识别分类方案。通过现场拍摄海滩垃圾图片,建立垃圾数据库;改进的YOLO-v4算法在传统的YOLO-v4网络架构SCPDarkNet53上融入混合空洞卷积结构,增强感受域的连续性,降低信息在池化过程中造成信息丢失的程度。引入空间锯齿空洞卷积,获取更多细节特征,将数据集导入改进后YOLO-v4架构中,实现图像中垃圾种类的识别。实验研究表明,相比YOLO-v4通用算法,所提算法在识别海滩垃圾的准确率提升了6%,对海滩环境的保护有一定的推广意义。展开更多
In this study,we extensively evaluated the viability of the state-of-the-art YOLOv8 architecture for object detec-tion tasks,specifically tailored for smoke and wildfire identification with a focus on agricultural and...In this study,we extensively evaluated the viability of the state-of-the-art YOLOv8 architecture for object detec-tion tasks,specifically tailored for smoke and wildfire identification with a focus on agricultural and environmen-tal safety.All available versions of YOLOv8 were initially fine-tuned on a domain-specific dataset that included a variety of scenarios,crucial for comprehensive agricultural monitoring.The‘large’version(YOLOv8l)was se-lected for further hyperparameter tuning based on its performance metrics.This model underwent a detailed hyperparameter optimization using the One Factor At a Time(OFAT)methodology,concentrating on key param-eters such as learning rate,batch size,weight decay,epochs,and optimizer.Insights from the OFAT study were used to define search spaces for a subsequent Random Search(RS).The final model derived from RS demon-strated significant improvements over the initial fine-tuned model,increasing overall precision by 1.39%,recall by 1.48%,F1-score by 1.44%,mAP@0.50 by 0.70%,and mAP@0.50:0.95 by 5.09%.We validated the enhanced model's efficacy on a diverse set of real-world images,reflecting various agricultural settings,to confirm its ro-bustness in detecting smoke and fire.These results underscore the model's reliability and effectiveness in scenar-ios critical to agricultural safety and environmental monitoring.This work,representing a significant advancement in the field of fire and smoke detection through machine learning,lays a strong foundation for fu-ture research and solutions aimed at safeguarding agricultural areas and natural environments.展开更多
文摘海滩垃圾日益增多,流入大海不但会对海洋环境造成污染,而且会影响海洋生物健康。对海滩垃圾进行识别并分类处理具有重要意义。提出基于改进的(you only look once version4,YOLO-v4)目标检测算法的垃圾识别分类方案。通过现场拍摄海滩垃圾图片,建立垃圾数据库;改进的YOLO-v4算法在传统的YOLO-v4网络架构SCPDarkNet53上融入混合空洞卷积结构,增强感受域的连续性,降低信息在池化过程中造成信息丢失的程度。引入空间锯齿空洞卷积,获取更多细节特征,将数据集导入改进后YOLO-v4架构中,实现图像中垃圾种类的识别。实验研究表明,相比YOLO-v4通用算法,所提算法在识别海滩垃圾的准确率提升了6%,对海滩环境的保护有一定的推广意义。
文摘In this study,we extensively evaluated the viability of the state-of-the-art YOLOv8 architecture for object detec-tion tasks,specifically tailored for smoke and wildfire identification with a focus on agricultural and environmen-tal safety.All available versions of YOLOv8 were initially fine-tuned on a domain-specific dataset that included a variety of scenarios,crucial for comprehensive agricultural monitoring.The‘large’version(YOLOv8l)was se-lected for further hyperparameter tuning based on its performance metrics.This model underwent a detailed hyperparameter optimization using the One Factor At a Time(OFAT)methodology,concentrating on key param-eters such as learning rate,batch size,weight decay,epochs,and optimizer.Insights from the OFAT study were used to define search spaces for a subsequent Random Search(RS).The final model derived from RS demon-strated significant improvements over the initial fine-tuned model,increasing overall precision by 1.39%,recall by 1.48%,F1-score by 1.44%,mAP@0.50 by 0.70%,and mAP@0.50:0.95 by 5.09%.We validated the enhanced model's efficacy on a diverse set of real-world images,reflecting various agricultural settings,to confirm its ro-bustness in detecting smoke and fire.These results underscore the model's reliability and effectiveness in scenar-ios critical to agricultural safety and environmental monitoring.This work,representing a significant advancement in the field of fire and smoke detection through machine learning,lays a strong foundation for fu-ture research and solutions aimed at safeguarding agricultural areas and natural environments.
