Tiny-RetinaNet: a one-stage detector for real-time object detection

In this paper, we present a new one stage detector for object detection. In order to meet the requirements of real-time detection, we use MobileNetV2-FPN as backbone for feature extraction. The lightweight depthwise separable convolutions can improve the speed of detection and make the model smaller. In order to improve the accuracy of our small detection model, we add Stem Block into backbone and we add SEnet in front of two task-specific subnets. The stem block can reduce the information loss from raw input images. The SEnet can enhance useful features from backbone network and suppress features that are little use to two-specific tasks. Inspired by RetinaNet, we also use Focal Loss as our classification loss function. We measure our performance on PASCAL VOC2007 and PASCAL VOC2012. Our detector with 300300 input achieves 73.8% mAP on VOC2007 test, 71.4% mAP on VOC2012 test. And our detector can run at 97FPS and the number of parameters is only 7.7M that meets the requirements of real-time detection. The accuracy of our detector is close to SSD, our detector uses about only 1/3 parameters to SSD. Keywords: One-

[1]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[4]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[5]  Forrest N. Iandola,et al.  SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[6]  Alexander Wong,et al.  Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection , 2018, 2018 15th Conference on Computer and Robot Vision (CRV).

[7]  Gang Sun,et al.  Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Jian Cheng,et al.  Quantized Convolutional Neural Networks for Mobile Devices , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[13]  Andrew Zisserman,et al.  Speeding up Convolutional Neural Networks with Low Rank Expansions , 2014, BMVC.

[14]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[15]  Charles X. Ling,et al.  Pelee: A Real-Time Object Detection System on Mobile Devices , 2018, NeurIPS.

[16]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[17]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[18]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[19]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Xiangyu Zhang,et al.  ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  Nuno Vasconcelos,et al.  Cascade R-CNN: Delving Into High Quality Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.