Special faster-RCNN for multi-objects detection

A series of neural networks called RCNN are playing a vital role in objects detection, as the most perfect one, Faster RCNN achieved an end-to-end object detection and made the detection times comparatively low but with high accuracy. In this work, we propose the following two changes to the original Faster RCNN model for multi-object detection: The first, we give 1800 ROI(Regions of Interest) comes from RPN to the RCNN network as input instead of 300, all the 1800 ROI are used to training the softmax classification and Bounding-box regression. The second, we traverse all xml files of every training image to get the number of marked objects and calculate the value of IOU for every marked objects, then we set a dynamic loss function to evaluation and optimization the Faster RCNN model by the two values of an image.