期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
排水泵站内漂浮垃圾的自动收集装置
1
作者 梁良 于伟胜 +2 位作者 袁继祖 顾士杰 袁述时 《中文科技期刊数据库(全文版)工程技术》 2019年第6期230-230,232,共2页
如今各泵站、污水处理厂内均配备有先进的格栅除污机设备,可以有效地进行池底沉淀垃圾的清捞工作。但漂浮于水面的垃圾却不能有效处理,导致很多漂浮垃圾(图1)随水流一同进入了水泵内然后至排放口排出,导致水泵故障频发,更影响了出水口... 如今各泵站、污水处理厂内均配备有先进的格栅除污机设备,可以有效地进行池底沉淀垃圾的清捞工作。但漂浮于水面的垃圾却不能有效处理,导致很多漂浮垃圾(图1)随水流一同进入了水泵内然后至排放口排出,导致水泵故障频发,更影响了出水口的出水质量。由于现在对于浮面垃圾的收集大都采用人工清捞,极少部分使用螺旋输送清捞专职进行垃圾收集,由于人工清捞极和螺旋输送装置很大程度上耗费时间,效率极低;而且螺旋输送装置设备体积较大,具有比较大的安装局限性,同时在浮面垃圾的清捞收集过程中还容易出现垃圾缠绕,卡住螺旋轴的情况,导致设备故障,影响平时的泵站运营,不利于管理。本论文将主要针对浮面垃圾特点和清捞要求,阐述说明一种全新的漂浮垃圾自动收集与升降装置,来完成井内浮面垃圾的收集清捞和运输,同时规避现有螺旋输送装置的设备弊端。 展开更多
关键词 浮面垃圾 网桶机构 吸水机构 自动升降 低故障率
下载PDF
Improved YOLOv7 Algorithm for Floating Waste Detection Based on GFPN and Long-Range Attention Mechanism
2
作者 PENG Cheng HE Bing +1 位作者 XI Wenqiang LIN Guancheng 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2024年第4期338-348,共11页
Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus result... Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus resulting in a degradation of detection performance.In order to tackle these challenges,a floating waste detection algorithm based on YOLOv7 is proposed,which combines the improved GFPN(Generalized Feature Pyramid Network)and a long-range attention mechanism.Firstly,we import the improved GFPN to replace the Neck of YOLOv7,thus providing more effective information transmission that can scale into deeper networks.Secondly,the convolution-based and hardware-friendly long-range attention mechanism is introduced,allowing the algorithm to rapidly generate an attention map with a global receptive field.Finally,the algorithm adopts the WiseIoU optimization loss function to achieve adaptive gradient gain allocation and alleviate the negative impact of low-quality samples on the gradient.The simulation results reveal that the proposed algorithm has achieved a favorable average accuracy of 86.3%in real-time scene detection tasks.This marks a significant enhancement of approximately 6.3%compared with the baseline,indicating the algorithm's good performance in floating waste detection. 展开更多
关键词 floating waste detection YOLOv7 GFPN(Generalized Feature Pyramid Network) long-range attention
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部