The CNN model was also applied to geological structure image classification. As [PERSON] et al. [7] showed, transfer learning based on a deep learning model is effective to extract features of geological structure data. Oil spill detection on the ocean surface has been a hot issue, and a refined technique is required to detect dark spots on SAR (synthetic aperture radar) images. A deep convolution neural network, called Segnet has been tested for this application by [PERSON] et al. [8]. It has the basic framework of encoder and decoder for image semantic segmentation. It improves accuracy performance to extract an oil spill location and area. Three-dimensional image modeling reconstructs the 3D model of an object with multiple 2D images as well as maintaining its texture. [PERSON] et al. [9] proposed a texture mapping method to use mesh partitioning, mesh parameterization and packing, texture transferring, and texture correction and optimization. It forms a high-resolution texture map of a 3D model for application in e-commerce. Image super-resolution technique is one of the most promising CNN based image processing techniques, which can be applied to various kinds of images. [PERSON] et al. [10] propose a dual-channel CNN super-resolution network to extract the detailed texture information (deep channel) and the overall outline of the original image (shallow channel). Image quality assessment is an important process to maintain good quality of images for various types of imaging system. [PERSON] et al. [11] proposed a new method of automatically assessing image quality when several enhancement techniques are applied, by using feature sets derived from high dynamic range images. Detection-specific AI techniques such as the region-based CNN (R-CNN) can be used for various industrial purposes with specific configuration of a vision system. [PERSON] et al. [12] proposed a novel one-camera-five-mirror system for cavitation bubble cluster study and have used a faster R-CNN method to detect bubbles in their system. The concept of residual network has widely been adopted in various deep learning architectures. [PERSON] et al. [13] proposed an efficient deep residual network approach by using sparse feedbacks, which improves the convergence speed and the training stability in their image restoration applications. Image segmentation has been tackled with a novel active contour model by [PERSON] et al. [14]. It uses an improved SPF (signed pressure function) and a LIF (local image fitting) model. A weight function of the grayscale mean values around the contour curve was introduced to segment blurred images and weak gradient images, and another metric function was used to check local image information to segment intensity-inhomogeneous images. Image segmentation plays a role of monitoring the weld pool surface affecting the weld quality. The reflected laser lines used for arc welding contain the weld pool surface information, and [PERSON] et al. [15] proposed that various image processing methods are combined for image segmentation approach, applied to the reflected laser lines. An effective image search is demanding work in the field of content-based image retrieval. A novel approach to encode the relative spatial information for histogram-based representation of the visual worlds has been introduced by [PERSON] et al. [16]. It computes the geometric relationship in the visual worlds and enhances the performance of image retrieval. Surface defect detection is a challenging problem in industrial product manufacturing. Vision-based defect detection of steel sheets focuses on finding the salient characteristics of the defects. [PERSON] et al. [17] processed the defect segmentation with a double low-rank and sparse decomposition model to obtain high-quality defect images. Another style of surface defect detection can be found in the railway surface. A novel visual inspection approach based on UAV (unmanned aerial vehicle) images has been tested by [PERSON] et al. [18].