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1篇 您的检索式：作者名="V.Arulalan"

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		题名	作者	年代	出处	被引量
	1	Efficient Object Detection and Classification Approach Using HTYOLOV4 and M^(2)RFO-CNN显示文摘Object detection and classiﬁcation are the trending research topics in theﬁeld of computer vision because of their applications like visual surveillance.However,the vision-based objects detection and classiﬁcation methods still suffer from detecting smaller objects and dense objects in the complex dynamic envir-onment with high accuracy and precision.The present paper proposes a novel enhanced method to detect and classify objects using Hyperbolic Tangent based You Only Look Once V4 with a Modiﬁed Manta-Ray Foraging Optimization-based Convolution Neural Network.Initially,in the pre-processing,the video data was converted into image sequences and Polynomial Adaptive Edge was applied to preserve the Algorithm method for image resizing and noise removal.The noiseless resized image sequences contrast was enhanced using Contrast Limited Adaptive Edge Preserving Algorithm.And,with the contrast-enhanced image sequences,the Hyperbolic Tangent based You Only Look Once V4 was trained for object detection.Additionally,to detect smaller objects with high accuracy,Grasp conﬁguration was observed for every detected object.Finally,the Modiﬁed Manta-Ray Foraging Optimization-based Convolution Neural Network method was carried out for the detection and the classiﬁcation of objects.Comparative experiments were conducted on various benchmark datasets and methods that showed improved accurate detection and classiﬁcation results.	V.Arulalan Dhananjay Kumar	2023	Computer Systems Science & Engineering2023，44，2：	0

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