A MASK-RCNN Based Approach Using Scale Invariant Feature Transform Key points for Object Detection from Uniform Background Scene
Keywords:Bigdata, CNN, Deep Learning, Feature points, Mask-RCNN, SIFT
Object identification using deep learning in known environment gives a new dimension to the research area of computer vision based automation system. As it uses supervised learning technique using Convolution Neural Network (RCNN) it helps automation software tools and machines to detect and identify objects using vision based systems. One of RCNN technique known as Mask-RCNN has been applied in this proposed design and this paper presents a novel approach to object detection problem using Big Data storage for large set of features based data. Earlier work Faster Region-based CNN has led to the development of a state-of-the-art object detector termed as Mask R-CNN. Some samples of solid material objects used in refractory industry have been taken as input images. In our experiment the SIFT based features have been implemented and trained using filter and convolution operation. In addition to improved accuracy, pixel-level annotation (annotating bounding boxes is approximately an order of magnitude which is quicker to perform). The model is retrained to perform the detection of four types of metal objects, with the entire process of annotation and training for the new model per solid block. A key benefit of feature based Mask-RCNN approach is high precision (~94%) in classification and minimized feature points with SIFT key points.
(1) Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, Neural Networks and signal processing, 2009 IEEE.
(2) Marcos Eduardo Valle, Complex-Valued Recurrent Correlation Neural Networks ,IEEE Transactions on Neural Networks and Learning SystemsVolume: 25, Issue: 9,Pages: 1600 - 1612,Year: 2014
(3) Liang Zhang, Peiyi Shen , Guangming Zhu , Wei Wei , and Houbing Song, A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor, Sensors 2015, 15, 19937-19967; doi:10.3390/s150819937
(4) Chen, C.; Liu, M.-Y.; Tuzel, C.O.; Xiao, J., R-CNN for Small Object Detection, TR2016-144, November 2016, Mitsubishi Electric Research Laboratories
(5) Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun,"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 39, No. 6, June 2017
(6) Cheng Wang, Ying Wang, Yinhe Han, Lili Song, Zhenyu Quan, Jiajun Li and Xiaowei Li,"CNN-based object detection solutions for embedded heterogeneous multicore SoCs", 2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC)
(7) Kaiming He Georgia Gkioxari Piotr Dollar Ross Girshick,"Mask R-CNN", Facebook AI Research (FAIR), April 2017.
(8) Subhransu Maji, Alexander C. Berg, and Jitendra Malik, “Efficient Classification for Additive Kernel SVMs”, Transactions On Pattern Analysis And Machine Intelligence, Vol. 39, No. 6, June 2017
(9) Cheng Wang, Ying Wang, Yinhe Han, Lili Song, Zhenyu Quan, Jiajun Li and Xiaowei Li,"CNN-based object detection solutions for embedded heterogeneous multicore SoCs", 2017 22nd Asia and South Pacific Design
Automation Conference (ASP-DAC)
(10) Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall Wald and Edin Muharemagic, Deep learning applications and challenges in big data analytics ,Journal of Big Data 2015
(11) Xiaojiang Peng, Cordelia Schmid, Multi-region two-stream R-CNN for action detection, European Conference on Computer Vision, Oct 2016, Amsterdam, Netherland
(12) Sapan Naik,Bankim Patel, Machine Vision based Fruit Classification and Grading -A Review, International Journal of Computer Applications (0975 –8887) Volume 170 –No.9, July 2017
(13) R Dalai, KK Senapati, Comparison of Various RCNN techniques for Classification of Object from Image , International Research Journal of Engineering and Technology (IRJET), Volume: 04, Issue: 07, July -2017
(14) T. Hoang Ngan Le, Yutong Zheng, Chenchen Zhu, Khoa Luu, Marios Savvides, Multiple Scale Faster-RCNN Approach to Driver’s Cell-Phone Usage and Hands on Steering Wheel Detection ,IEEE Conference on Computer
Vision and Pattern Recognition Workshops (CVPRW), 2016
(15) Sezer Karaoglu, Yang Liu, Theo Gevers, Detect2Rank: Combining Object Detectors Using Learning to Rank,
IEEE Transactions on Image Processing, Year: 2016, Volume: 25, Issue: 1,Pages: 233 – 248
(16) Wanli Ouyang, Xingyu Zeng, Xiaogang Wang, Shi Qiu, Ping Luo, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Hongyang Li, Kun Wang, Junjie Yan, Chen-Change Loy, Xiaoou Tang, "DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks",IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 39, Issue: 7,Pages: 1320 – 1334, Year: 2017
(17) Eel-Wan Lee, Soo-Ik Chae, Fast design of reduced-complexity nearest-neighbor classifiers using triangular inequality, IEEE Transactions on Pattern Analysis and Machine Intelligence Volume: 20, Issue: 5, Pages: 562 - 566, Year: 1998
(18) Ross Girshick, Fast R-CNN Object detection with Caffe , Microsoft Research.
Web resource : https://en.wikipedia.org/wiki/Feature_hashing