Improved Faster R-CNN for Multi-Scale Object Detection

For multi-scale object detection, the detection methods based on single-level feature extraction suffered from the low detection quality because of the loss or distortion of feature for small-scale objects, or the redundancy of feature for large-scale objects. We propose a multi-scale object detection method based on Faster R-CNN. The method extracts the multi-scale features with the policy of multi-level feature extraction, configures statistically the size and the aspect ratio of the anchor, and adopts a multi-channel region strategy to generate multi-scale proposals. Extensive experiments on the PASCAL VOC dataset show that the quality of our method, with 9.7% of the log-average miss rate and 75.2% of the mean average precision, performs better than the traditional detection methods.