Object Detection and Localization Using Sparse-FCM and Optimization-driven Deep Convolutional Neural Network

Abstract

Object detection and localization attract the researchers to address the challenges associated with the computer vision. The literature presents numerous unsupervised methods to detect and localize the objects, but with inaccuracies and inconsistencies. The problem is tackled through proposing a novel model based on the optimization algorithm. The object in the image is detected using the Sparse Fuzzy C-Means (Sparse FCM) that is the enhanced Fuzzy C-Means algorithm used to manage the high-dimensional data. The detected objects are subjected to the object localization, which is performed using the proposed Cat Crow Optimization (CCO)-based Deep Convolutional Neural Network. The proposed CCO is the integration of Cat Swarm Optimization Algorithm and Crow Search Algorithm and inherits the advantages of both the optimization algorithms. The experimentation of the proposed method is performed using images obtained from the Visual Object Classes Challenge 2012 dataset. The analysis revealed that the proposed method acquired an average accuracy, precision, and recall of 0.8278, 0.8549, and 0.7911, respectively.

Hybrid optimization enabled multi‐aggregator‐based charge scheduling of electric vehicle in internet of electric vehicles

“…12 Besides, the cooperative distributed technique, Monte Carlo, and multi-agent approach 15 are introduced for scalable scheduling solutions. 3 Moreover, different optimization 16,17 algorithms, 18 such as moving window optimization, 19 multi-agent-based system, 15 ant-based swarm algorithm, 20 genetic algorithm, 21 linear programming, 22 estimation of distribution algorithm, 23 Tabu search (TS) and greedy randomized adaptive search procedure (GRASP), 24 and particle swarm optimization (PSO) 25 are applied for electric vehicle charge scheduling process.…”