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Automated image and video object detection based on hybrid heuristic-based u-net segmentation and faster region-convolutional neural network-enabled learning

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MuLTIMEDIA TOOLS AND APPLICATIONS 2023年第3期82卷 3459-3484页

作者： Palle, Rajashekar Reddy Boda, Ravi Koneru Lakshmaiah Educ Fdn KLEF Dept ECE Hyderabad Telangana India

Object detection is one of the major areas of computer vision, which adopts machine learning approaches in diverse contributions. Nowadays, the machine learning field has been directed through Deep Neural networks (DNNs) that takes eminent features of progressions in data availability and computing power. In all the cases, the quality of images and videos are biased and noisy, and thus, the distributions of data are also considered as imbalanced and disturbed. Different techniques are developed for solving the abovementioned challenges, which are mostly considered based on deep learning and computer vision. Though, traditional algorithms constantly offer poor detection for dense and small objects and yet fail the detection of objects through random geometric transformations. One of the categories of deep learning called Convolutional Neural network (CNN) is famous and well-matched method for image-related tasks, in which the network is trained for discovering the numerous features like colour differences, corners, and edges in the images and videos that are combined into more complex shapes. This proposal intends to develop improved object detection in images and videos with the advancements of deep learning models. The three main phases of the proposed object detection model are (a) pre-processing, (b) segmentation, and (c) detection. Once the pre-processing of the image is performed by median filtering approach, the adaptive u-net segmentation is performed for the object segmentation using the newly proposed Sun Flower-Deer Hunting Optimization Algorithm (SF-DHOA). The maximization of segmentation accuracy and dice coefficient is considered as the main objective of the proposed segmentation. The hybrid meta-heuristic algorithm termed SF-DHOA is proposed with Sun Flower Optimization (SFO) and Deer Hunting Optimization Algorithm (DHOA), which is used for optimally tuning the u-net by optimizing the encoder depth and the number of epoch. Further, the detection is per

关键词： Object detection Deep learning Convolutional neural network adaptive u-net segmentation Sun flower-deer hunting optimization algorithm Modified faster region-convolutional neural network

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