Various object detection algorithms and their comparison

Abstract

This paper presents a detailed and comparative analysis of various object detection algorithms. The challenge of object detection is taken care of while studying various algorithms. Throughout the year various methods have been discovered in this field, each having its advantages and drawbacks. This paper aims at providing the systematic study of all the popular algorithms including the conventional ones. Although many methods and techniques come up each year and each of them having superiority other the previous models but at the same time even complexity increases. In this paper, some famous and basic methods of object detection and tracking are discussed. Using these developed techniques good results can be obtained and also the comparison of the efficiency of all the models can be done. Real-time applications and the outcomes are also discussed.

Adaptive Stochastic Conjugate Gradient Optimization for Backpropagation Neural Networks

“…The algorithm dynamically adjusts the learning rate and step size based on the gradient information, allowing it to navigate the complex parameter space more efficiently [6]. By adaptively updating the conjugate directions, ASCG avoids being trapped in local minima and accelerates the convergence of the optimization process [7]. The importance of ASCG optimization in training BPNNs lies in its ability to handle large-scale datasets and complex learning tasks.…”