Object Detection based on Region Decomposition and Assembly

Understand

Region-based object detection infers object regions for one or more categories in an image.

• Due to the recent advances in deep learning and region proposal methods, object detectors based on convolutional neural networks (CNNs) have been flourishing and provided the promising detection results.
• However, the detection accuracy is degraded often because of the low discriminability of object CNN features caused by occlusions and inaccurate region proposals.
• In this paper, we therefore propose a region decomposition and assembly detector (R-DAD) for more accurate object detection.