Research On Edge Detection Algorithm Of Thermal Infrared Image Based On PiDiNet

With the wide application of thermal infrared image technology in various industries,it makes the research related to thermal infrared image processing a hot spot,but the problems of low contrast and blurred edges in thermal infrared images hinder the development of related research.And edge information as an important part of image information,acquiring image edge information is the basis for studying image processing.At present,there is relatively little research on edge algorithms for thermal infrared images,so this paper constructs an edge detection algorithm for thermal infrared images based on PiDiNet model.The main work is as follows:(1)To address the problems of small and poor quality edge detection datasets for thermal infrared images.This paper builds on the visible dataset BSDS500 and combines the characteristics of thermal infrared images.The thermal infrared image edge dataset is constructed by graying out the visible image,adding noise and reducing contrast to provide data for model training and testing.(2)Based on exploring the structure of convolutional neural network models such as convolutional layer,pooling layer,activation layer,and batch normalization layer,this paper delves into the theory of PiDiNet model and provides theoretical basis for improving the network from the step-by-step analysis of the backbone network and the side network of this model.(3)Improve the PiDiNet model by adding a rich convolution mechanism to the backbone network,improving the utilization efficiency of the pixel difference convolution layer in the backbone network,modifying the activation function to improve the efficiency of the model for feature extraction,and modifying the pooling layer to improve the ability of the backbone network for feature extraction;study the SimAM attention mechanism and add it to the side network,and modify the upsampling module to improve the key regions.(4)Using the open source thermal infrared image dataset to provide images for testing,and analyzing different detection algorithms by experimental comparison,and further proving the superiority of this paper’s algorithm compared with other algorithms based on the results of image evaluation parameters,while testing by taking thermal infrared images,the parameter results show that the detection performance of this paper’s algorithm is higher than other algorithms.