Abstract: An improved algorithm of dangerous goods detection based on Adaboost and LBP is proposed, which can improve the accuracy and speed of identification. It solves the problem that the detection accuracy of dangerous goods decreases due to the influence of brightness, illumination and other interference factors. The improved algorithm incorporates HSV color space classification of positive samples in the training stage, which improves the detection efficiency of cascade classifiers, and extracts eigenvalues with the improved LBP algorithm. Compared with traditional object detection methods, the accuracy is improved by 2 percent point to 93.29%. Finally, the algorithm is transplanted to the rescue manipulator platform. The experimental results show that the improved detection algorithm can identify dangerous goods accurately and quickly in the actual environment detection, and the training efficiency is obvious. At the same time, it has good robustness under different illumination conditions and meets the practical requirements.

Key words: dangerous objects detection, Adaboost, LBP, HSV color space, cascade classifier, service and rescue robots