Fine-grained object detection based on self-adaptive anchors

Abstract

The fine-grained object detection is an extremely challenging problem due to the subtle variances in the appearances. At present, faster R-CNN is one of the best detection systems. However, it not a wise decision to directly apply the faster R-CNN to the fine-grained object detection. By analyzing the characteristics of fine-grained objects, we found that the anchor mechanism in the faster R-CNN system has a lot of redundancy. By analyzing the characteristics of fine-grained objects, we use self-adaptive anchors to enhance the structure of the system and combine the detection and classification of fine-grained objects. By using self-adaptive anchors, new progress has been made on the small-scale fine-grained datasets (Stanford Cars).We making the detection of mean average precision on the Stanford Cars dataset flush to 88.9%. And we notice that this mechanism used in non-fine-grained detection does not decrease its effect. So this mechanism, which is named self-adaptable anchors, can be used as a general idea in object detection.