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Deep neural network has been widely applied in the field of object detection. However, due to the poor interpretability and other technical deficiencies of deep neural network, it is easy to be invalidated by external interference. Full research on adversarial attack methods is helpful to explore the reasons for the invalidation of deep neural network and improve its robustness. At present, most of the adversarial attack methods need to use the gradient information of the model or the confidence information of the model output, but the object detectors used in the industry usually do not fully disclose their internal information and confidence information, so the existing white box attack methods are no longer applicable. To enhance the robustness of industrial object detector, this paper proposes a decision-based black box adversarial attack method for object detector. The characteristics of this method does not need to use gradient information and confidence information of the model, only the use of the object detector output detection box position information. The strategy of this method is to make the object detector locate wrong and attacks it, and to find the optimal adversarial examples by iterative search along the adversarial boundary so as to achieve efficient attack. Experimental results show that the proposed method reduces mAR from 0.636 to 0.131 and mAP from 0.801 to 0.071 on VOC2012 data set of typical object detector Faster R-CNN, effectively reducing the detection ability of object detector and successfully achieving black box attack on the object detector.