Edge detection is the process of extracting the important information of mutations in the image. It is a research hotspot in the field of computer vision and the basis of many middle-and high-level vision tasks such as image segmentation, target detection and recognition. In recent years, in view of the problems of thick edge contour lines and low detection accuracy, edge detection algorithms based on deep learning such as spectral clustering, multi-scale fusion, and cross-layer fusion were proposed by the industry. In order to make more researchers understand the research status of edge detection, firstly, the implementation theory and methods of traditional edge detection were introduced. Then, the main edge detection methods based on deep learning in resent years were summarized, and these methods were classified according to the implementation technologies of the methods. And the analysis of the key technologies of these methods show that the multi-scale multi-level fusion and selection of loss function was the important research directions. Various methods were compared to each other through evaluation indicators. It can be seen that the Optimal Dataset Scale (ODS) of edge detection algorithm on the Berkeley Segmentation Data Set and benchmark 500 (BSDS500) was increased from 0.598 to 0.828, which was close to the level of human vision. Finally, the development direction of edge detection algorithm research was forecasted.

In order to solve the problem of object drift caused by Kernelized Correlation Filter (KCF) tracking algorithm when scale changes, a Fast Scale Adaptive tracking of Correlation Filter (FSACF) was proposed. Firstly, a global gradient combination feature map based on salient color features was obtained by directly extracting features for the original frame image, reducing the effect of subsequent scale calculation on the performance. Secondly, the method of separating window was performed on the global feature map, adaptively selecting the scale and calculating the corresponding maximum response value. Finally, a defined confidence function was used to adaptively update the iterative template function, improving robustness of the model. Experimental result on video sets with different interference attributes show that compared with KCF algorithm, the accuracy of the FSACF algorithm by was improved 7.4 percentage points, and the success rate was increased by 12.8 percentage points; compared with the algorithm without global feature and separating window, the Frames Per Second was improved by 1.5 times. The experimental results show that the FSACF algorithm avoids the object drift when facing scale change with certain efficiency, and is superior to the comparison algorithms in accuracy and success rate.

Concerning the problem that traditional crack detection algorithm based on percolation model has low efficiency and detection results are prone to fracture, a crack detection algorithm based on multi-factor decision and percolation model was proposed. Firstly, an improved algorithm of accelerating crack inspection based on percolation model was proposed, which improves the efficiency of percolation processing by reducing a large number of redundant pixel points involved in percolation processing. Secondly, the extracted percolation points were used to percolation processing. Finally, a multi-factor decision connection algorithm based on crack orientation was proposed. In the algorithm, the rationality of crack connection was analyzed by four decision factors to improve the accuracy of crack connection. Different morphological crack images with different interfering objects in background were used in experiments. Compared with traditional percolation model detection algorithm and original algorithm of accelerating crack inspection based on percolation model and skeleton connection algorithm, the number of percolation points of the proposed algorithm was reduced by an average of 99.7% and 38.1%, respectively. The precision was increased by an average of 60.5% and 6.4%, respectively, and the recall was increased by an average of 10.5% and 4.0%, respectively. The experimental results show that the proposed algorithm can significantly improve the efficiency of percolation processing and improve the accuracy of crack detection.