In the practical application scenario, the wheat kernel image background is single, and the imperfect characteristics of wheat imperfect grains are mostly local features while most of the image features are not different from normal grains. In order to solve the problems, an imperfect wheat kernel recognition method based on detail Image Enhancement (IE) was proposed. Firstly, the alternate minimization algorithm was used to constrain the L0 norms of the original image in the horizontal and vertical directions to smooth the original image as the base layer, and the original image was subtracted from the base layer to obtain the detail layer of the image. Then, the detail layer was delighted and superimposed with the base layer to enhance the image. Finally, the enhanced image was used as the input of the Convolutional Neural Network (CNN), and the CNN with Batch Normalization (BN) layer was used for recognition of the image. The classic classification networks LeNet-5, ResNet-34, VGG-16 and these networks with the BN layer were used as classification networks, and the images before and after enhancement were used as input to carry out classification experiments, and the accuracy of the test set was used to evaluate the performance. Experimental results show that by adding the BN layer and using the same input, all three classic classification networks have the accuracy of the test set increased by 5 percentage points, and when using the images with enhanced detail as input, the three networks have the accuracy of the test set increased by 1 percentage point, and when the above two are used together, all the three networks obtain the accuracy of the test set improved by more than 7 percentage points.

In order to solve the problems such as background interference, illumination variation and occlusion between targets in people counting of actual public scene videos, a new people counting method combined with feature map learning and first-order dynamic linear regression was proposed. Firstly, the mapping model of feature map between the Scale-Invariant Feature Transform (SIFT) feature of image and the target true density map was established, and the feature map containing target and background features was obtained by using aforementioned mapping model and SIFT feature. Then, according to the facts of the less background changes in the monitoring video and the relatively stable background features in the feature map, the regression model of people counting was established by the first-order dynamic linear regression from the integration of feature map and the actual number of people. Finally, the estimated number of people was obtained through the regression model. The experiments were performed on the datasets of MALL and PETS2009. The experimental results show that, compared with the cumulative attribute space method, the mean absolute error of the proposed method is reduced by 2.2%, while compared with the first-order dynamic linear regression method based on corner detection, the mean absolute error and the mean relative error of the proposed method are respectively reduced by 6.5% and 2.3%.

In order to remove the moving cast shadow in the surveillance video quickly and efficiently, an adaptive shadow elimination method based on superpixel and local color constancy of shaded area was proposed. First, the improved simple linear iterative clustering algorithm was used to divide the moving area in the video image into non-overlapping superpixels. Then, the luminance ratio of background and the moving foreground in the RGB color space was calculated, and the local color constancy of shaded area was analyzed. Finally, the standard deviation of the luminance ratio was computed by taking superpixel as basic processing unit, and an adaptive threshold algorithm based on turning point according to the characteristic and distribution of the standard deviation of the shadowed region was proposed to detect and remove the shadow. Experimental results show that the proposed method can process shadows in different scenarios, the shadow detection rate and discrimination rate are both more than 85%; meanwhile, the computational cost is greatly reduced by using the superpixel, and the average processing time per frame is 20 ms. The proposed algorithm can satisfy the shadow removal requirements of higher precision, real-time and robustness.