ThunderNet

2000년 도입 · 논문 4편에서 사용

ThunderNet is a two-stage object detection model. The design of ThunderNet aims at the computationally expensive structures in state-of-the-art two-stage detectors. The backbone utilises a ShuffleNetV2 inspired network called SNet designed for object detection. In the detection part, ThunderNet follows the detection head design in Light-Head R-CNN, and further compresses the RPN and R-CNN subnet. To eliminate the performance degradation induced by small backbones and small feature maps, ThunderNet uses two new efficient architecture blocks, Context Enhancement Module (CEM) and Spatial Attention Module (SAM). CEM combines the feature maps from multiple scales to leverage local and global context information, while SAM uses the information learned in RPN to refine the feature distribution in RoI warping.

출처: ThunderNet: Towards Real-time Generic Object Detection

소개 논문: ThunderNet: Towards Real-time Generic Object Detection