A convolutional neural network design called MobileNetV2 aims to function well on mobile devices. It is built on an inverted residual structure where the bottleneck layers are connected by residual connections. Lightweight depthwise convolutions are used in the intermediate expansion layer as a source of nonlinearity to filter features.
Published in Chapter:
Nighttime Object Detection: A Night-Patrolling Mechanism Using Deep Learning
V. Dinesh Reddy (SRM University, India), Sai Vishnu Vamsi Senagasetty (SRM University, India), Krishna Teja Vanka (SRM University, India), Mohana Vamsi Dhara (SRM University, India), Rupini Durga Puvvada (SRM University, India), and Muzakkir Hussain (SRM University, India)
Copyright: © 2023
|Pages: 28
DOI: 10.4018/978-1-6684-6937-8.ch024
Abstract
These days, it's becoming harder to feel safer when we go out at night. So, to tackle this security problem, the authors propose a night patrolling mechanism to detect objects in low light conditions. Images taken during the nighttime have difficulties with less contrast, brightness, and noise owing to inadequate light or insufficient exposure. Deep learning-based methods accomplish end-to-end, unsupervised object recognition using convolutional neural networks, which abolishes the requirement to describe and draw out attributes separately. Despite the fact that deep learning has led to the invention of many successful object detection algorithms; many state-of-the-art object detectors, like Faster-RCNN and others, can't carry out at their best under low-light situations. Even with an extra light source, it is hard to detect the features of an item due to the uneven division of brightness. This chapter proposes a deep learning algorithm called single shot detector, with Mobilenet v2 as the backbone to tackle the issues of object detection under low-light situations.