Detection of Waste Containers Using Computer Vision

This work is a part of an ongoing study to substitute the identification of waste containers via radio-frequency identification. The purpose of this paper is to propose a method of identification based on computer vision that performs detection using images, video, or real-time video capture to identify different types of waste containers. Compared to the current method of identification, this approach is more agile and does not require as many resources. Two approaches are employed, one using feature detectors/descriptors and other using convolutional neural networks. The former used a vector of locally aggregated descriptors (VLAD); however, it failed to accomplish what was desired. The latter used you only look once (YOLO), a convolutional neural network, and reached an accuracy in the range of 90%, meaning that it correctly identified and classified 90% of the pictures used on the test set.

[1]  S. Abdoli,et al.  RFID Application in Municipal Solid Waste Management System , 2009 .

[2]  Jianwei Niu,et al.  Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting , 2016, Pattern Recognit..

[3]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Maria Grazia Gnoni,et al.  A feasibility study of a RFID traceability system in municipal solid waste management , 2013, Int. J. Inf. Technol. Manag..

[5]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[6]  Qi Tian,et al.  SIFT Meets CNN: A Decade Survey of Instance Retrieval , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Andrew Zisserman,et al.  All About VLAD , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

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

[10]  Ehab Salahat,et al.  Recent advances in features extraction and description algorithms: A comprehensive survey , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[11]  Joseph Redmon,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

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

[13]  Andrea Zanella,et al.  Internet of Things for Smart Cities , 2014, IEEE Internet of Things Journal.

[14]  Krystian Mikolajczyk,et al.  Evaluation of local detectors and descriptors for fast feature matching , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[15]  K Kranthi Kumar,et al.  CBIR: Content Based Image Retrieval , 2010 .

[16]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[18]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[19]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[20]  Qi Tian,et al.  Recent Advance in Content-based Image Retrieval: A Literature Survey , 2017, ArXiv.

[21]  John Tran,et al.  cuDNN: Efficient Primitives for Deep Learning , 2014, ArXiv.

[22]  Zhigang Zhu,et al.  Mobile Panoramic Vision for Assisting the Blind via Indexing and Localization , 2014, ECCV Workshops.