%0 Journal Article %1 HaoZhang2021 %A Hao Zhang, %A gong Hong, Xiang %A Zhu, Li %D 2021 %J Automatic Control and Computer Sciences %K Deconvolution Dilation Module,SSD,object and detection,small image objects,thermal %N 2 %P 202--211 %R 10.3103/S0146411621020097 %T Detecting Small Objects in Thermal Images Using Single-Shot Detector %V 55 %X Abstract: SSD (Single Shot Multibox Detector) is one of the most successful object detectors for its high accuracy and fast speed. However, the features from shallow layer (mainly Conv4_3) of SSD lack semantic information, resulting in poor performance in small objects. In this paper, we proposed DDSSD (Dilation and Deconvolution Single Shot Multibox Detector), an enhanced SSD with a novel feature fusion module which can improve the performance over SSD for small object detection. In the feature fusion module, dilation convolution module is utilized to enlarge the receptive field of features from shallow layer and deconvolution module is adopted to increase the size of feature maps from high layer. Our network achieves 79.7% mAP on PASCAL VOC2007 test and 28.3% mmAP on MS COCO test-dev at 41 FPS with only 300 × 300 input using a single Nvidia 1080 GPU. Especially, for small objects, DDSSD achieves 10.5% on MS COCO and 22.8% on FLIR thermal dataset, outperforming a lot of state-of-the-art object detection algorithms in both aspects of accuracy and speed.

@article{HaoZhang2021, abstract = {Abstract: SSD (Single Shot Multibox Detector) is one of the most successful object detectors for its high accuracy and fast speed. However, the features from shallow layer (mainly Conv4_3) of SSD lack semantic information, resulting in poor performance in small objects. In this paper, we proposed DDSSD (Dilation and Deconvolution Single Shot Multibox Detector), an enhanced SSD with a novel feature fusion module which can improve the performance over SSD for small object detection. In the feature fusion module, dilation convolution module is utilized to enlarge the receptive field of features from shallow layer and deconvolution module is adopted to increase the size of feature maps from high layer. Our network achieves 79.7% mAP on PASCAL VOC2007 test and 28.3% mmAP on MS COCO test-dev at 41 FPS with only 300 × 300 input using a single Nvidia 1080 GPU. Especially, for small objects, DDSSD achieves 10.5% on MS COCO and 22.8% on FLIR thermal dataset, outperforming a lot of state-of-the-art object detection algorithms in both aspects of accuracy and speed.}, added-at = {2023-02-27T14:12:01.000+0100}, author = {{Hao Zhang} and gong Hong, Xiang and Zhu, Li}, biburl = {https://www.bibsonomy.org/bibtex/2e47f22e3c07579e4a9fa566e3e8163cd/haozhangcn}, doi = {10.3103/S0146411621020097}, file = {:F\:/OneDrive - nuaa.edu.cn/mendeley/2021-05-14_ddssd.md.pdf:pdf}, interhash = {e4503e7153dcf5372783417d17118db3}, intrahash = {e47f22e3c07579e4a9fa566e3e8163cd}, issn = {1558108X}, journal = {Automatic Control and Computer Sciences}, keywords = {Deconvolution Dilation Module,SSD,object and detection,small image objects,thermal}, number = 2, pages = {202--211}, timestamp = {2023-02-27T14:12:15.000+0100}, title = {{Detecting Small Objects in Thermal Images Using Single-Shot Detector}}, volume = 55, year = 2021 }