AquaVision: Real-Time Identification of Microbes in Freshwater Using YOLOv3

Yadav, Prajakta; Dhokrat, Siddhant Prakash; Kadam, Ajit; Gadakh, Prashant

doi:10.1007/978-981-16-5301-8_33

Cited by 1 publication

(1 citation statement)

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“…In our approach, we have used YOLO, often called a clever CNN, because it is superfast and supposed to be run in real-time [21][22]. It incorporates features learned by a Deep Convolutional Neural Network for detecting the objects it was first defined by Joseph Redmon and Ali Farhadi in the seminal 2015 paper.…”

Section: Yolo V3mentioning

confidence: 99%

Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN

Sahar

Khadami²,

Rauf³

2023

SJET

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Smart city infrastructure and Intelligent Transportation Systems (ITS) need modern traffic monitoring and driver assistance systems such as autonomous traffic signal detection. ITS is a dominant research area among several fields in the domain of artificial intelligence. Traffic signal detection is a key module of autonomous vehicles where accuracy and inference time are amongst the most significant parameters. In this regard, the aim of this study is to detect traffic signals focusing to enhance accuracy and real-time performance. The results and discussion enclose a comparative performance of a CNN-based algorithm YOLO V3 and a handcrafted technique that gives insight for enhanced detection and inference in day and night light. It is important to consider that real-world objects are associated with complex backgrounds, occlusion, climate conditions, and light exposure that deteriorate the performance of sensitive intelligent applications. This study provides a direction to propose a hybrid technique for TLD not only in the daytime but also in night light.

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Section: Yolo V3mentioning

confidence: 99%