关键词: 卷积神经网络, 深度学习, 级联网络, 高精度目标检测

Abstract:

As an important research direction of computer vision, object detection plays an increasingly important role in smart cities and unmanned driving. In traditional object detection algorithms, it judges positive and negative samples according to the threshold value of the Intersection over Union（IOU）. Some lower IOU will introduce noise and reduce the accuracy of the detector, and higher IOU will retain some high-quality samples, resulting in over-fitting. The difference between the IOU threshold of the recommended area and the detector will cause quality mismatch. In response to these problems, this paper proposes a parallel cascaded detection network based on cascaded network, which is composed of various of detectors connected in series and in parallel. Every of each detector sets an incremental IOU threshold, thus at each stage a higher quality distribution is obtained to train the next detector and gradually resample to reduce overfitting. The experimental results show that the proposed parallel cascaded detection network is better than the traditional object detection algorithm in detection precision. The average Accuracy Precision（AP） of the object detection dataset Microsoft COCO is increased by about 1.5 percentage points using the proposed parallel detection network.

Key words: convolutional neural network, deep learning, cascaded network, high-precision object detection