Tunnel cracks are the main factors that cause damage and collapse of tunnel structures. How to detect tunnel cracks efficiently and avoid safety accidents caused by tunnel cracks effectively is a research hotspot at present. In order to meet the need for efficient detection of tunnel cracks, the tunnel crack detection method based on improved Retinex and deep learning is proposed in this paper. The tunnel crack images collected by optical imaging equipment are used to improve the contrast information of tunnel crack images using the image enhancement algorithm, and this image enhancement algorithm has the function of multi-scale Retinex decomposition with improved central filtering. An improved VGG19 network model is constructed to achieve efficient segmentation of tunnel crack images through deep learning methods and then form the segmented binary image. The Zhang–Suen fast parallel-thinning method is used to obtain the skeleton map of the single-layer pixel, and the length and width information of the tunnel cracks are obtained. The feasibility and effectiveness of the proposed method are verified by experiments. Compared with other methods in the literature, the maximum deviation in the length of the tunnel crack is about 5 mm, and the maximum deviation in the width of the tunnel crack is about 0.8 mm. The experimental results show that the proposed method has a shorter detection time and higher detection accuracy. The research results of this paper can provide a strong basis for the health evaluation of tunnels.

In response to the issues of relatively low image contrast, blurry boundaries of infrared small targets, and various environmental noise interferences in thermal imaging systems, resulting in reduced target recognition rates and tracking effectiveness. This paper proposes an infrared target image recognition algorithm based on fuzzy comprehensive assessment. Built upon infrared image sequences captured by an on-board thermal imager, the algorithm employs grayscale processing, neighborhood mean filtering, and second-order difference method to filter and enhance the infrared target images. This process improves the quality of infrared target images and enhances the contrast between targets and backgrounds. The algorithm utilizes the maximum interclass variance method for segmenting the infrared target images, combining it with the Canny operator to extract target edges, thereby obtaining clearer details of the infrared target edges. Finally, based on the extracted target feature information, the algorithm calculates the confidence of each marked region, establishes the membership function of target likelihood, employs a comprehensive weighted approach to construct the target confidence function, and compares the confidence of marked regions with that of the template image to achieve precise target recognition. Experimental validation against theoretical methods and comparisons with other approaches demonstrate the effectiveness and feasibility of the proposed method, providing theoretical support for reliable target tracking in electro-optical imaging systems.

The accuracy of single-mode optical imaging systems for vehicle target recognition is limited by external ambient illumination or imaging equipment resolution. In this paper, a vehicle target recognition method based on visible and infrared image fusion using Bayesian inference is proposed. Based on the significance area detection combined with Spectral Residual (SR) and EdgeBox algorithms, the target area is marked on the visible light image, and the maximum between-class variance method and GrabCut algorithm are used to segment vehicle targets in marked images. The open operation filter is employed to extract the vehicle target features in the infrared image, and the fusion result of the visible light image and the infrared image is obtained by introducing the Intersection Over Union (IOU), and as the parameter of Bayesian inference, the class and attribute of the parameter are defined and substituted into the established naive Bayesian classification model, and the probability of the class is calculated to determine whether the vehicle target is recognized. Experiments under different test conditions were carried out, and the experimental results were as follows: the accuracy of image target recognition reached 77% when the vehicle target was not occluded; when the vehicle target was partially occluded, the accuracy of image target recognition reached 74%; the results verified that the proposed method can recognize vehicle targets in different scenarios.

With the rapid development of laser fuze technology, the ability of fuze to recognize target has become increasingly important. To effectively improve the recognition rate of laser fuze against interference signal and target signal, based on the two-channel symmetric laser scanning detection system, this paper proposes a classification method based on fuzzy c-means incremental update under the condition of small sample test data. The classification model is established using the fuzzy c-means algorithm, which uses the time-domain entropy, frequency-domain entropy and frequency domain exponential entropy of the reflected laser echo signal as the feature variables; The classification model is adaptively adjusted by adopting an improved fuzzy c-means incremental update algorithm to obtain the high accuracy classification results; Combining with the target’s signal features extracted by discrete binary wavelet transform, the classification results of real target signal are optimally recognized by wavelet neural network. The experimental results show that compared with the wavelet neural network recognition method, the target recognition method proposed in this paper based on fuzzy c-means incremental update and neural network can achieve higher target recognition rate under conditions of low signal-to-noise ratio and high threshold signal-to-noise ratio. Moreover, fuzzy c-means incremental update method reduces time-consuming of classification model, significantly improves the anti-interference ability, the results provide reference significance for future applications of neural network recognition method in laser fuze detection systems.