关键词: 目标探测, 卷积神经网络, 目标定位, 条件概率, 边界框

Abstract: As traditional object detection methods are generally applied to object detection systems with low accuracy, a novel object localization methodology is proposed to boost localization accuracy of state-of-the-art object detection systems. Firstly, the initial set of candidate boxes is confirmed. Secondly, conditional probabilities are assigned for each row and column of the search region, in which these probabilities provide useful information regarding boundaries?location?of?object inside searching region. According to the probabilities, the paper builds in-out probabilities, border probabilities and combined probabilities three models to return the bounding box of an object of interest inside this region to achieve object localization. Finally, combined with the localization model, a convolutional neural network architecture is adopted to train and detect objects in combination with localization model. The experiments for different IoU threshold on PASCAL VOC and COCO datasets are carried out. Experimental result show that the proposed method exhibits better detection accuracy in comparison with traditional methods, and could be applied to the state-of-the-art object detection systems. Furthermore, sliding windows method is applied to generate initial set of candidate boxes, not only simplifies the process of confirming the initial set, but also maintains high detection accuracy, which illustrates the proposed method is completely independent of the traditional bounding box regression paradigm.

Key words: object detection, convolutional neural network, object localization, conditional probabilities, bounding box