An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.

To improve the laser cleaning efficiency of Q235 carbon steel, an imaging analysis-based intelligent technique is proposed. Both offline and online computations are designed. Regarding the offline procedure, first, the corrosion images are accumulated to compute the gray-level co-occurrence matrix (GLCM) and the concave-convex region features. Second, different laser cleanings are performed to obtain various cleaned images. Third, a new cleaning performance evaluation method is developed: a metal color difference feature and a dynamic weight dispatch (DWD) corrosion texture are computed. Finally, a particle swarm optimization (PSO)-support vector machine (SVM) is utilized to forecast the laser process parameters. The corresponding laser parameters include power, linear velocity, and line spacing. For the online computation, after the GLCM and the concave-convex region features are computed, an iterative computation is used to tune the process parameters: the random laser parameters are generated constantly, and the iteration is performed and terminated only if the PSO-SVM output is positive. The experimental results have shown that the cleaning efficiency of this method can be improved, and the qualified rate is 92.5%.

A face recognition system that uses 3-D lighting estimation and optimal lighting compensation for dark-field application is proposed. To develop the proposed system, which can realize people identification in a near scene dark-field environment, a light-emitting diode (LED) overhead light, eight LED wall lights, a visible light binocular camera, and a control circuit are used. First, 68 facial landmarks are detected, and their coordinates in both image and camera coordinate systems are computed. Second, a 3-D morphable model (3DMM) is developed after considering facial shadows, and a transformation matrix between the 3DMM and camera coordinate systems is estimated. Third, to assess lighting uniformity, 30 evaluation points are selected from the face. Sequencing computations of LED radiation intensity, ray reflection luminance, camera response, and face lighting uniformity are then carried out. Ray occlusion is processed using a simplified 3-D face model. Fourth, an optimal lighting compensation is realized: the overhead light is used for flood lighting, and the wall lights are employed as meticulous lighting. A genetic algorithm then is used to identify the optimal lighting of the wall lights. Finally, an Eigenface method is used for face recognition. The results show that our system and method can improve face recognition accuracy by >10% compared to traditional recognition methods.

An image analysis-based two-stage process parameters tuning and Surface Roughness (SR) estimation algorithm is proposed for the laser cleaning application. A Cartesian coordinate robot is utilized to collect image and implement cleaning. Before cleaning, in order to tune the proper laser parameters, first, the environment lighting is controlled for the metal image collection. Second, lots of classification features are computed for the images above. The Gray-Level Co-occurrence Matrix (GLCM) texture features, the concavo-convex region features, the histogram symmetry difference feature, and the imaging thermophysical property features are computed. Third, the initial laser parameters are created randomly and an iteration computation is performed: a Support Vector Machine (SVM) is used to forecast the cleaning effect; its inputs include the classification features and the initial laser parameters; its output is the cleaning effect degree. If the SVM output cannot fulfill user’s demand, the laser parameters will be updated randomly. This iteration will be implemented constantly until the SVM output becomes valid. Then the laser cleaning will be performed. When estimating SR for the cleaned metal, multiple image features are calculated for the images after cleaning. The features include the Tamura coarseness, some GLCM features, and the convex region feature. To improve the prediction precision, different feature combinations are used for different cleaning effects. The linear function and the 3-order polynomial function are considered for the SR estimation. After tests, the accuracies of SVM, the SR prediction function, and the integrated SR control and estimation algorithm can be 90.0%, 80.0% and 80.0% approximately.

This paper presents a volume measurement method for belt conveyor based on binocular vision and line structured light. The line laser stripe images of coal flow surface are collected by binocular camera, and the surface height of coal flow is measured by extracting laser stripe. First, the calibrated binocular camera is used to collect the laser stripe images of coal flow. Second, the laser stripes are extracted from the images and the spatial coordinates of the points on it are calculated. Third, after the cross-sectional area of coal flow is calculated by laser stripe, the coal flow volume can be obtained by integrating with the belt transmission speed. Two foam models are used to verify the accuracy of the system. The average error of volume measurement is less than 2%.

An integrated calibration method of the imaging sensor and the fiber optic taper-caused distortion in an intensified Complementary Metal-Oxide-Semiconductor (CMOS) camera for the X-ray capture application is proposed. Generally, four kinds of distortions need to be corrected in this system: the column noise, the inconsistent pixel intensity response of whole frame, the nonlinear intensity response of each pixel, and the geometry distortion of whole frame. To conquer these problems, first, the column noise is estimated and subtracted from the captured image. The multiple measurement data processing method and the strip noise estimation technique are used. Second, a kind of two-step correction technique of the pixel intensity response is proposed to solve middle two distortion problems. The first step builds the ideal intensity response line for all pixels in image; and the second step estimates and compensates the correction error for each pixel by a polynomial function. Third a polynomial fitting method is also utilized to correct the geometry distortion. The RANdom SAmple Consensus (RANSC) computation mechanism is used to improve the calibration precision further. The optic indices of fiber optic taper and camera are considered to assist the selection of key parameters of these calibration method above. Many experiment results have proved the correctness and effectiveness of proposed method.