Improve object detection via a multi-feature and multi-task CNN model

Abstract

Current state-of-the-art object detection methods have made dramatic performance improvements in recent few years. However, there are still several challenges. In particular, it still struggles for precise localization of small-sized objects, mainly due to coarse resolutions of feature maps and excessive surroundings such as ground and water. To address the issues, we propose an object detection system based on standard Fast R-CNN object detection branch and DeepLap semantic segmentation branch: (1) multi-feature aggregates hierarchical features for more finer feature maps to detect objects at multiple scales. (2) multi-task uses semantic segmentation for more contextual information to assist object detection via a cross structure between the two tasks. (3) a novel overlap loss function is used for bounding box regression that adjusts region proposals to improve localization. The fusion network improves results over the Fast R-CNN baseline detector by 2.8% mAP and by 4.8% mAP for small objects based on PASCAL VOC datasets.