Open AccessCascade single stage Detector using full convolution network
Author(s) -
Ming Liu,
Zhengfa Yu,
Kai Fu,
Xin Zhou
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012020
Subject(s) - pascal (unit) , cascade , computer science , object detection , detector , inference , convolution (computer science) , artificial intelligence , stage (stratigraphy) , computation , algorithm , multi stage , single shot , pattern recognition (psychology) , artificial neural network , engineering , telecommunications , optics , paleontology , chemical engineering , process engineering , biology , programming language , physics
For object detection, the two-stage approach always achieves better performance than one-stage. But one-stage is more effective. The main shortcomings of one-stage approach are: class imbalance problem during the training phase and sometimes the correct object score is lower. To address those problems, we propose a novel single-shot detector, called Cascade detection. It is composed of several stages, and each stage is composed of several detection layers. The main point of our method is: we design an anchors to anchors module (A2AM), which gives one-stage approach the ability of training stage by stage. It can not only address the class imbalance problem in the training phase, but also increase the object scores and the Iou between prediction and ground truth in the inference phase. The multitask loss function enables us to train the whole network in an end-to-end way. Experiments on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO prove that our method achieves state-of-art performance detection accuracy and computation efficiency.