To automatically detecting whether a person is wearing mask properly, we propose a face mask detection algorithm based on hue-saturation-value (HSV)+histogram of oriented gradient (HOG) features and support vector machines (SVM). Firstly, human face and five feature points are detected with RetinaFace face detection algorithm. The feature points are used to locate to mouth and nose region, and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not. Secondly, RetinaFace is used to locate to nasal tip area of face, and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area, and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results. Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%, and the accuracy of detecting whether mask is worn correctly can reach 87.55%, which verifies the feasibility of the algorithm.

In order to solve the problem of path planning of tower cranes, an improved ant colony algorithm was proposed. Firstly, the tower crane was simplified into a three-degree-of-freedom mechanical arm, and the D-H motion model was established to solve the forward and inverse kinematic equations. Secondly, the traditional ant colony algorithm was improved. The heuristic function was improved by introducing the distance between the optional nodes and the target point into the function. Then the transition probability was improved by introducing the security factor of surrounding points into the transition probability. In addition, the local path chunking strategy was used to optimize the local multi-inflection path and reduce the local redundant inflection points. Finally, according to the position of the hook, the kinematic inversion of the tower crane was carried out, and the variables of each joint were obtained. More specifically, compared with the traditional ant colony algorithm, the simulation results showed that improved ant colony algorithm converged faster, shortened the optimal path length, and optimized the path quality in the simple and complex environment.