Q
Qiang Bai
Researcher at Guizhou University
Publications - 8
Citations - 175
Qiang Bai is an academic researcher from Guizhou University. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 2, co-authored 8 publications receiving 20 citations.
Papers
More filters
Journal ArticleDOI
Review of Image Classification Algorithms Based on Convolutional Neural Networks
TL;DR: In this paper, a review of the application of CNNs to image classification tasks is presented, which focuses on their development, from their predecessors up to recent state-of-the-art (SOAT) network architectures.
Journal ArticleDOI
Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey
TL;DR: According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping.
Journal ArticleDOI
Object Detection Method for Grasping Robot Based on Improved YOLOv5
TL;DR: In this article, an object detection method for grasping robots based on improved YOLOv5 was proposed in order to achieve more accurate positioning and recognition of objects, in which the object detection platform was designed, and the wooden block image data set was being proposed.
Journal ArticleDOI
Intelligent Optimization Algorithm-Based Path Planning for a Mobile Robot.
TL;DR: In this paper, a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO) was proposed.
Journal ArticleDOI
Trajectory Planning of Robot Manipulator Based on RBF Neural Network
TL;DR: In this article, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error.