Energy Efficient Illegal Dumping Learning System Akshay Dabholkar,Bhushan Muthiyan, Shilpa Srinivasan, Swetha Ravi Project Advisor: Dr. Hyeran Jeon Department of Computer Engineering, San José State University, San José, California 95192 Abstract Technologies used Block Diagram Methodology Results and Analysis Approach 2 had fair accuracy due to clean area misprediction. Illegal dumping has become an issue of major concern to the City of San Jose in the recent years. A major reason for this is due to the high fees that imposed by recycling companies to pick-up the waste. This has led to rise in disposing of household wastes like mattresses, couches, broken appliances, discarded clothes along streets, parks, and under the freeway bridges. This illegal dumping could pose the serious threat to the safety and well-being of the residents as well as that of the environment. The bacteria and odor emitted from the waste could be the carrier for dreadful diseases and thereby pose a serious health hazard to the citizens. At times these wastes could even disrupt normal traffic flow on the roads causing serious accidents and crimes. The existing system to track and identify the illegal dumping is manual, tedious, and inefficient. It involves continuous physical monitoring of illegal dumping hotspots by the city personnel. This process is strenuous and uneconomical. We propose to automate this process by using Neural Networks to detect illegal dumping automatically so that the strenuous task of continuous physical monitoring is eliminated. A camera is interfaced with NVIDIA Jetson TX1- GPU Board to capture field images. ‘Caffe’-a deep-learning framework was used to train the Neural Network model. The model is trained and tested by supplying a database of illegal dump images. The system uses these trained models to monitor the real-time field data. Deep Learning 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 methods 2. Neural Network and related framework 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. Hence a good candidate for GPU. Neural Network framework used in this project is Caffe. 3. Computer Vision OpenCV preprocessing is done to crop and the objects from the raw image taken from the hotspot before giving it to the Caffe model for the classification to have better classification and to match with our training methodology. 4. Energy Efficiency We used NVIDIA TensorRT that helps shrink model size. TensorRT helps reduce the model size mainly by using quantization and horizontal-vertical layer fusion. We explored four different approaches to achieve higher recognition accuracy, which are explained in detail below. We used TensorRT, which comes with Jetpack 2.3.1 to shrink model size for the edge computing. We conducted our experiments on existing reference models like AlexNet and GoogLeNet. Approach 1 (Naive Approach) 6 classes Iterations: 100 Image dataset size:161 Mb The accuracy was not great and hence was revised by adding more classification. Approach with more classes: 11 classes Baseline Network: AlexNet and Googlenet Iterations: 5000 Image dataset size: 802 Mb 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. Approach with pre-processed images Cropping image dataset for better training. 8 classes Iterations: 10000 Image dataset size: 1.14 Gb Approach with increased classification and pre-processing 11 classes with detection for human, vehicles and animals. Baseline Network: Googlenet Iterations: 40000 Image dataset size: 2.1 Gb Approach 3 Approach 3 had good accuracy for all the classes. Approach 4 Approach 4 covered the detection of human ,vehicle and animals which are the possible entities that could be present on the street. Thus, to avoid them being classified as illegal dumping we trained for these classes as well. Model compression using TensorRT Methodology Background To reduce the illegal waste dumping, many cities run various education programs, social-media-based community portals, surveillance camera monitoring, and execute policies that impose fines to the dumpers. Current illegal dumping detection and removal system is inefficient and costly because the city personnel should continuously monitor a few hot spots manually and there are many false alarms in citizens’ dumping reports. Our approach provides a fully automated illegal dumping detection and reporting. The project is implemented in two phases namely, training phase and testing phase. In training phase, the CNN is trained with large dataset of images for each class in which images are required to be classified. Caffe, which is used to train the CNN requires images to be transformed in a format. For this, the raw images are converted into Lightening Memory Mapped Database (LMDB) format. During the testing phase, the model is tested with predetermined image dataset to test the results of image classification done by the trained CNN model. Notification about the prediction is handled by setting up a client which communicates details as to which class the image belongs and image details to a remote server. Frequently illegal dumped objects can be classified into following broad category: Chair, Furniture, Table, Trash Bags, Sofa, Electronics, Vehicles, Trash, Trees, Clean Area, Cart, Human and Animals. Results and Analysis Approach 1 Conclusions We demonstrated that the illegal dumping can be automatically detected by using deep learning methodology. We explored several design trade-offs to achieve better accuracy with smaller memory footprint. Our suggested deep learning model achieves 40%-100% accuracy with only 12MB memory footprint. System Block Diagram In Approach 1, sofa and mattress classes delivered 60% and 55% accuracies, respectively, while the other classes achieved lower than 30%. Other classes had limited accuracy since the properties of those objects demanded greater feature map. Acknowledgements We are highly indebted to Dr. Hyeran Jeon for her continued guidance and support during the entire course of the project. We would like to extend our gratitude to Prof. Jerry Gao and Prof. Xiao Su. We would like to thank San Jose Innovation Centre for providing us with the necessary resources to carry out field testing. We would also like to thank the City of San Jose for extending their support and acknowledging our work. Approach 2 The system includes NVIDIA Jetson Tx1 interfaced to RGB USB-based CCTV camera. Classes identified for training the model.