A LIGHTWEIGHT TARGET DETECTION ALGORITHM BASED ON MOBILENET CONVOLUTION
DOI:
https://doi.org/10.26906/SUNZ.2023.2.119Keywords:
deep learning, MobileNet, global Average pooling layer, GPU, target detectionAbstract
Target detection algorithm based on deep learning needs high computer GPU configuration, even need to use high performance deep learning workstation, this not only makes the cost increase, also greatly limits the realizability of the ground, this paper introduces a kind of lightweight algorithm for target detection under the condition of the balance accuracy and computational efficiency, MobileNet as Backbone performs parameter The processing speed is 30fps on the RTX2060 card for images with the CNN separator layer. The processing speed is 30fps on the RTX2060 card for images with a resolution of 320×320.Downloads
References
Howard A G , Zhu M , Chen B , et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Appl. [J]. 2017.
Girshick R . Fast R-CNN[J]. Computer Science, 2015.
He K , Gkioxari G , P Dollár, et al. Mask R-CNN [C]// IEEE. IEEE, 2017.
Redmon J , Farhadi A . YOLO9000: Better, Faster, Stronger[C] // IEEE Conf. on Computer Vision & Pattern Recognition. IEEE, 2017:6517-6525.
Redmon J , Farhadi A . YOLOv3: An Incremental Improvement[J]. arXiv e-prints, 2018.
He K , Zhang X , Ren S , et al. Deep Residual Learning for Image Recognition[J]. IEEE, 2016.
Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017.
Goodfellow I J. Generative Adversarial Networks[J]. Advances in Neural Information Processing Systems, 2014, 3:2672-2680.
Technicolor T , Related S , Technicolor T , et al. ImageNet Classification with Deep Convolutional Neural Networks [50].
Simonyan K , Zisserman A . Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. Computer Science, 2014.
Pang Y , Sun M , Jiang X , et al. Convolution in Convolution for Network in Network[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, PP(99):1587-1597.
GoogLenet [J]. Journal of Thesis of Korean Cultural Information Society, 2018, 18.
He K , Zhang X , Ren S , et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification[C]// CVPR. IEEE Computer Society, 2015.
Li H , Xiong P , Fan H , et al. DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020.
Wang H , Jiang X , Ren H , et al. SwiftNet: Real-time Video Object Segmentation[J]. 2021.
Li Wei,Liu Kai. Confidence-Aware Object Detection Based on MobileNetv2 for Autonomous Driving[J]. Sensors,2021,21(7).
Batra Varun,Kumar Vijay. Real-Time Object Detection and Localization for Vision-Based Robot Manipulator[J]. SN Computer Science,2021,2(3).