High Precision Object Segmentation And Its Applications In Industry

Object segmentation has always been a core problem in the field of computer vision,and it is also a difficult problem.Over the years,researchers have conducted extensive research on object segmentation,but have not been able to achieve satisfactory results.The deep learning technology that emerged in recent years has pushed the performance of object segmentation to a new level and has been widely used in many industrial scenarios.However,most existing object segmentation models focus on the pursuit of higher detection performance(such as m AP,etc.).For many relatively single industrial scenarios,higher segmentation accuracy is more important than detection performance,such as industrial measurement and industrial defect detection,etc.Aiming at this situation,this paper makes an in-depth study on object segmentation models,implements a high-precision object segmentation model based on deep learning,and attempts to apply the model to industrial scenarios.The specific application scenarios based on the research and experiments in this paper include high-voltage power transmission fields such as insulator defect analysis and screw potential defect detection.In this paper,through extensive research on object detection and segmentation algorithms based on deep learning,the advantages and disadvantages of object segmentation models based on deep learning are analyzed,techniques and methods to improve segmentation accuracy are discussed,and an end-to-end high precision transmission tower insulator segmentation model is proposed.The paper has conducted a lot of tests and analysis on the proposed high-precision segmentation model of insulators.The results show that the model can achieve 93.75% m IOU on the custom insulator object segmentation test set,and the optimized model can achieve nearly 95% m IOU,exceeds mainstream object segmentation models such as Mask R-CNN.Experimental results also show that the speed performance and accuracy performance of the model are close to the requirements of actual industrial scenes.This paper also tests and analyzes the screw detection and defect recognition based on the deep learning method,which further proves the adaptability and practicability of the object segmentation model proposed in this paper.