Smart Illegal Dumping Detection - PowerPoint Presentation

Slide1

Smart Illegal Dumping Detection

Akshay

Dabholkar, Bhushan Muthiyan, Shilpa Srinivasan, Swetha Ravi, Hyeran Jeon, Jerry GaoDepartment of Computer EngineeringSan Jose State UniversitySan Jose, CaliforniaSlide2

Illegal Dumping

Illegal dumping has been a chronic problem in many cities in the world.

Illegal dumping makes our communities unsafe and it is expensive for the city to remove those items.

To reduce the illegal dumping, a few cities have designed community-based voluntary reporting systems and surveillance camera-based monitoring systems.Slide3

Existing Work

Los Angeles Health and

Sanitation

Chicago Clean Drive

Breathe Project PittsburghAir LouisvilleBeautify San Jose

Ref :http://datasmart.ash.harvard.edu/news/article/monitoring-air-quality-and-the-impacts-of-pollution-679

https://www.airlouisville.com/Slide4

SpotGarbage

Mobile application based solution to tackle garbage litter in India.

Key difference between our approach and theirs is on the image dataset and litter they cater to.

Ref :https://github.com/

KudaP/SpotGarbageSlide5

Our ApproachPropose to use deep learning technique to recognize various types of frequently dumped wastes with high recognition accuracy and small memory footprint.Slide6

Our GoalSlide7

Technologies UsedSlide8

Deep Learning - The new trend setter

Deep learning is a multilayered, back propagating, representational, self-learning method implemented by using simple but non-linear functions.Deep learning is the part of the machine learning based on the representational learning methodsSlide9

Neural network

The fundamental element of the deep learning or machine learning is the artificial neuron.

Calculations for learning are performed by these neurons.

Different types are

Feed ForwardRadial Basis FunctionMulti Layer PerceptronRecurrent Neural NetworkSelf Organizing MapAnd many more…..

Ref : http://cs231n.github.io/neural-networks-1/ https://www.extremetech.com/extreme/215170-artificial-neural-networks-are-changing-the-world-what-are-theySlide10

Convolutional Neural Network (CNN)

Convolutional Neural Network (CNN) is a practically useful algorithm especially in computer vision tasks by reduced complexity with resolving overfitting problems from deep learning.

CNN inside is made of simple repeated matrix multiplications without branch operations.

Ref:https

://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/Slide11

Neural Network Frameworks

Ref: http://blog.algorithmia.com/deploying-deep-learning-cloud-services/Slide12

MethodologyCreate illegally dumped material labeled dataset.

Create lmdb database from the dataset.Tweak the existing model as per need and train the model using Caffe interface with the generated

lmdb files.Deploy the model file on the embedded platform Jetson TX1Test the model for prediction accuracy.Slide13

Illegal dump object classification

We classified the illegal dumped objects into following broad category :

Chair

MattressTableSofaFurnitureTrashSlide14

Proposed approaches

Approach 1 (Naive Approach)Frequently dumped wastes identified in 102 illegal dumping images provided by City of San Jose

6 classes

Baseline Network: AlexNet

Iterations: 100Image dataset size:161 MbSlide15

Prediction Accuracy of Approach 1 Slide16

This was insufficient…

Solution : Add more categories and train for Clean Area detection.Slide17

Increased Classification

Electronics

Trees

Cart

Clean AreaSlide18

Approach 2

Approach with more classes: 11 classes

Baseline Network: AlexNet and

GooglenetIterations: 5000

Image dataset size: 802 MbSlide19

Prediction Accuracy of Approach 2Slide20

Misprediction

The disadvantage of this approach was the model tends to predict clean area even for the image with illegal dump object because the feature map for clean areas was more compared to normal images.Slide21

Revise Approach

Solution Image pre-processing to clearly define the region of interest.

Excluding Clean Area classification.Classes AggregationSlide22

Approach 3

Approach with pre-processed imagesCropping image dataset for better training.

8 classes

Baseline Network: AlexNet and Googlenet

Iterations: 10000Image dataset size: 1.14 GbSlide23

Prediction Accuracy of Approach 3Slide24

Energy Efficiency Approach- TensorRT

We used NVIDIA TensorRT that helps shrink model size.

Uses two approaches to shrink trained model size. 1. Quantization, 2. Optimizations by applying vertical and horizontal layer fusion.

Ref: https://developer.nvidia.com/tensorrtSlide25

Model size variation using TensorRT

Approaches

Network

Original (in MB)

With

TensorRT

(in MB)

Approach 1

AlexNet

227.6

84.7

Approach 2

AlexNet

227.7

113.8

GoogleNet

41.4

12

Approach 3

AlexNet

227.6

113.8

GoogleNet

41.3

12Slide26

ConclusionDetect illegal dumping with over 90% accuracy and reduced memory consumption.

The model could be scaled to cover more classifications by training with more images of greater variety.Extensive pre-processing could be done to cover the adverse lighting conditions and thereby increase prediction accuracy.Slide27
Slide28

Thank youSlide29

Why Caffe?

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC

) and by community contributors.Expressive architecture

Extensible codeSpeed

Ref : http://caffe.berkeleyvision.org/Slide30

We have chosen Caffe as our baseline framework as it works

well for image recognition purposes.

Ref: https://www.slideshare.net/NiketanPansare/notes-from-2016-bay-area-deep-learning-schoolSlide31

Jetson TX1Slide32

References

[1] “About drive clean

chicago,” 2017. [Online].

Available:http://www.drivecleanchicago.com/About/What.aspx

[2] “#beautifysj,” 2017. [Online]. Available: http://www.beautifysj.org/[3] “Los angeles applications,” 2017. [Online]. Available: http://geohub.

lacity.org/

[4] “Streets

philadelphia

,” 2017. [Online]. Available: http://www.philadelphiastreets.com/philly-spring-cleanup/

[5] . A. R. M.

Ballester

, P., “On the performance of

googlenet

and

alexnet

applied to sketches,” The Thirtieth AAAI Conference on Artificial Intelligence,2016.

[6] I. G. Y.

Bengio

and A.

Courville

, “Deep learning,” 2016, book in preparation for MIT Press. Available:

http://www.deeplearningbook.org

.

[7] Y. J. P. S. S. R. D. A. D. E. V. V. A. R. Christian

Szegedy

, Wei Liu, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition, 2015.

[8] Y. Jia, E.

Shelhamer

, J. Donahue, S.

Karayev

, J. Long, R.

Girshick,S

.

Guadarrama

, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the 22Nd ACM International Conference on Multimedia, 2014, pp. 675–678.[9] Y. LeCun, B. Boser

, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural

Comput., vol. 1, no. 4, pp. 541–551, 1989.[10] A. Moujahid, “A practical introduction to deep learning with

caffe and python,” 2017. Available: http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/

[11] NVIDIA, “Nvidia tensorrt,” 2017. Available:

https://developer.nvidia.

com/

tensorrt

[12] J.

Salleh

and M.

Tsudagawa

, “Classification of industrial disposal illegal dumping site images by using spatial and spectral information together,” IEEE Instrumentation and Measurement Technology Conference, 2002.