A feature enriching object detection framework with weak segmentation loss

作者：Zhang, Tianqi; Hao, Li-Ying; Guo, Ge^*

来源：Neurocomputing, 2019, 335: 72-80.

DOI：10.1016/j.neucom.2019.01.018

• Object detection
• Convolutional neural network
• Semantic information

This paper proposes an object detection model named Feature Enriching Object Detection Framework with Weak Segmentation Loss (FEOD) based on convolutional neural networks, which is an improvement of You Only Look Once (YOLO). To overcome the shortcoming of insensitivity to small objects, a novel feature enriching module is proposed to augment the semantic information of the feature in the shallow layer within a typical deep detector. Meanwhile, Focal Loss is also introduced to our model to further improve the algorithm performance. To obtain features more suitable for object detection, a more powerful feature extractor - DetNet is used as the backbone. An mAP of 83.8 in VOC2007 test and an mAP of 82.1 in VOC2012 test are achieved with a FPS of 11.3 with a NVIDIA GTX1080Ti GPU. For a lower resolution version, we achieve an mAP of 81.9 and 80.9 respectively in VOC2007 and VOC2012 tests with FPS of 17. Comparisons with other object detection algorithms have shown that our method works well and achieves a trade-off of accuracy and speed.

• 出版日期2019-3-28
• 单位大连海事大学