摘要
由于输送带的运动速度快、煤流的形状和颜色变化大,并且光照条件复杂,传统的输送带煤流检测方法往往存在准确性不高、易受干扰等问题。为此,提出了一种基于超分辨率深度图像修复的输送带煤流检测算法。该算法采用YOLOv3作为基础框架,结合超分辨率深度图像修复模型,对模糊且含有噪声的煤流图像进行处理。图像修复模型通过编码器—解码器结构,对破损图像的特征进行提取和修复,同时保留浅层纹理信息并将其传递至深层。处理后的清晰煤流图像,通过基于YOLOv3的目标检测算法进行煤流检测。在北方某煤炭加工厂的试验结果表明:当图像破损度为50%时,相比于基于互编码器的图像修复模型,所提图像修复模型结构相似性提升了10%;相比于YOLOv4-tiny,所提煤流检测算法的处理速度提升了56帧/s,反映出该算法可有效提高输送带煤流检测效率。
Due to the high speed of the conveyor belt,the large variations in the shape and color of the coal flow,and the complex lighting conditions,traditional methods for detecting the coal flow on conveyor belts often suffer from low accuracy and are prone to interference.Therefore,a coal flow detection algorithm based on super-resolution depth image restoration is pro-posed.This algorithm uses YOLOv3 as the basic framework and combines a super-resolution depth image restoration model to process blurred and noisy coal flow images.The image restoration model,through an encoder-decoder structure,extracts and re-pairs the features of damaged images while preserving shallow texture information and passing it to deeper layers.The processed clear coal flow images are then detected using the YOLOv3-based object detection algorithm.Experimental results from a coal processing enterprise in Northern China show that when the image damage rate is 50%,the proposed image restoration model improves the structural similarity by 10%compared to the mutual encoder-based image restoration model.Compared to YOLOv4-tiny,the proposed coal flow detection algorithm increases the processing speed by 56 fps,demonstrating that this algo-rithm can effectively improve the efficiency of coal flow detection on conveyor belts.
作者
范巧艳
董洁
郭攀
FAN Qiaoyan;DONG Jie;GUO Pan(School of Mechanical and Electrical Engineering,Xi′an Vocational and Technical College,Xi′an 710077,China;College of Mathematics and Computer Science,Chifeng University,Chifeng 024000,China;School of Water Conservancy and Transportation,Zhengzhou University,Zhengzhou 450000,China)
出处
《金属矿山》
北大核心
2025年第7期166-171,共6页
Metal Mine
基金
河南省高等学校重点科研项目(编号:23ZX014)。
关键词
目标检测
输送带
煤流
超分辨率
图像修复
深度学习
object detection
conveyor belt
coal flow
super-resolution
image restoration
deep learning
