ing at the problems of deep network depth, low utilization rate of feature relationship and low time efficiency in existing pedestrian recognition algorithm based on deep learning, this paper proposes an improved method based on squeeze and excitation residual neural network (SE-ResNet) and feature fusion. By introducing the squeeze and excitation (SE) module, the features are compressed and excited on the feature channels, and then weights are assigned to each channel to enhance the useful feature channels and suppress the useless feature channels to reduce the depth of the network model. In order to improve the recognition accuracy and computing efficiency, shallow features and deep features are used, and feature extraction modules are deleted. The relationship between the size of convolution kernel and the running time and recognition accuracy is modeled to find the best balance point. Experimental results show that compared with ResNet50, the recognition accuracy of this algorithm is 4.26 percentage points higher, mean average accuracy value is 17.41 percentage points higher. Compared with other classic algorithms, the recognition accuracy of this algorithm has also been improved to varying degrees, and the robustness is better.

ing at the problem of skin lesion image segmentation, a skin lesion image segmentation is proposed based on multi-scale DenseNet. First, the morphological closing operation and the un-sharp filter are used to preprocess the original skin lesion image and to obtain a refinement image without hairs and blood-vessel artifacts. Then, the pre-processed image is input into a segmentation network. This network is based on an encoder-decoder architecture and uses two multi-scale feature fusion methods of parallel multi-branch structure and pyramid pooling block model to achieve feature extraction under different receptive fields. Furthermore, the DenseNet structure is integrated into the encoder to realize the reuse of image features, and the L_Total loss function which combines target loss and content loss is adopted to further improve the accuracy of image segmentation. Finally, the segmentation results are obtained through the SoftMax classifier and the related evaluation indicators are calculated. The experimental results on the ISBI 2016 skin lesion image dataset show that the accuracy, Dice coefficient, Jaccard index, sensitivity, and specificity are 95.48%, 96.37%, 93.41%, 92.93%, and 96.49%, respectively, and the whole performance here is better than those of the existing algorithms. The proposed algorithm can accurately segment skin lesions and thus it can be applied to the melanoma computer-aided diagnosis systems.

ing at resolving the issue of accuracy of the existing checkerboard corner detection algorithms, we propose a high-precision checkerboard corner detection algorithm based on Hough transform and circular template. First, we used the Hough transform to extract straight lines in an image, used the distribution features of the checkerboard lines to obtain the effective straight lines, and then obtained and roughly located the approximate corner points based on these lines. Second, we constructed a new circular template that is moved around the roughly located corner points to search for related points; we simultaneously obtained their image coordinates and observation distances. Finally, we solved more accurate corners, which should satisfy the minimum difference between the actual distances from related points and their observation distances. The experimental results reveal that the calibration error of the proposed method significantly reduces compared with the existing methods. When the illumination is not ideal, the proposed method can also realize accurate detection. This method provides a strong basis for the application of high-precision calibration of actual cameras.