Research Overview on Edge Detection Algorithms Based on Deep Learning and Image Fusion

Abstract

Edge detection is a boundary-based segmentation method to extract important information from an image, and it is a research hotspot in the fields of computer vision and image analysis. Especially feature extraction is also the basis of image segmentation, target detection, and recognition. In recent years, in order to solve the problems of edge detection refinement and low detection accuracy, the industry has proposed multiscale fusion wavelet edge, spectral clustering, network reconstruction, and other edge detection algorithms based on deep learning. In order to enable researchers to understand the current research status of edge detection, this paper first introduces the classic algorithm of traditional edge detection, compare with advantages and disadvantages of different edge detection algorithms. Then, it summarizes the main edge detection methods based on deep learning in recent years and classifies and compares them according to the implementation technology. Finally, it shows the development direction of edge detection algorithm research.

Real-Time Cucumber Target Recognition in Greenhouse Environments Using Color Segmentation and Shape Matching

“…Shape matching involves the use of a similarity metric function to compute the similarity between the template picture and the image being compared [30,31]. Matching is deemed successful when the similarity surpasses a predetermined level.…”