A deep learning approach based hardware solution to categorise garbage in environment

Abstract

Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet

“…[ 9 ] designed a smart waste bin based on ResNet34, which can dichotomize waste with a single inference time of 950 ms. Ref. [ 52 ] constructed a smart bin based on inceptionv3, which can recycle waste into two bins. These smart devices are based on large models for waste classification, which has significance for automatic waste sorting and recycling.…”

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

“…9 shows the sensor categories used for waste sorting operations thus far. Multiple cameras include near-infrared (NIR) hyperspectral camera [140], and Multiple sensors include ultrasonic [62], infrared [141], proximity [60], [93], and color sensors. The color sensor detects specific colors or color temperatures [60], [142].…”

Comparative Study of ML Algorithms for Garbage Classification

“…For each classi cation task, it loads the underlying InceptionV3 model that has already been pre-trained on ImageNet [17], eliminates the top classi cation layer, and inserts custom layers. A GlobalAveragePooling2D layer, a Dropout layer [19] for regularisation, a dense hidden layer activated by ReLU, and 6. Experiment, Results, and Discussion…”