Real-time Litter Recognition Using Improved YOLOv4 Tiny Algorithm
subject: fully connected layer; litter; object detection; prediction time; YOLOv4-Tiny
description: Littered roads have become a familiar sight in India. The main reason is the increasing population and inefficient waste disposal system. Since garbage collectors cannot pick litter in all the places, there is a need for an efficient way to detect it. Hence, a machine learning-based object detection model is used. In this, we have applied an improved YOLOv4-Tiny algorithm to detect the garbage, classify it and make the detection process easier on custom datasets. We have improved the algorithm in terms of the object prediction time, this is done by replacing a max pooling layer with one of two layers present in a fully connected layer. When an input is given, the algorithm detects the litter in the image with a bounding box around it along with the label and confidence score. The proposed model reduces the prediction time by 0.517 milliseconds less than the original algorithm employed which concludes that the object is predicted faster. 2022 IEEE.
creator: Shalini V.; Tangade S.; Prajna P.K.; Sangeetha J.P.; Azam F.; Anoop G.L.
source: MysuruCon 2022 - 2022 IEEE 2nd Mysore Sub Section International Conference
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022-01-01
rights: Restricted Access
relation: ISBN: 978-166549790-9
format: Online
language: English
type: Conference paper
coverage: Shalini V., Reva University, School of Electronics and Commn. Engg, Bangalore, India; Tangade S., Christ (Deemed to Be University), Dept. of Cse, Bangalore, India; Prajna P.K., Reva University, School of Electronics and Commn. Engg, Bangalore, India; Sangeetha J.P., Reva University, School of Electronics and Commn. Engg, Bangalore, India; Azam F., School of Comp. Science & Engineering, Reva University, Bangalore, India; Anoop G.L., Christ (Deemed to Be University), Dept. of Cse, Bangalore, India