ReviewSensors (Basel, Switzerland)2025
An Extensive Study of Convolutional Neural Networks: Applications in Computer Vision for Improved Robotics Perceptions.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection.Fundamental research · 2026Review
- Convergence of Multidimensional Sensing: A Review of AI-Enhanced Space-Division Multiplexing in Optical Fiber Sensors.Sensors (Basel, Switzerland) · 2026Review
- Achieving Robotic Data Efficiency Through Machine-Centric FDCT Vision Processing.Sensors (Basel, Switzerland) · 2026Article
- Research on return water temperature prediction model for casting cooling system based on deep learning.Scientific reports · 2026Article
- An improved facial emotion recognition system using convolutional neural network for the optimization of human robot interaction.Scientific reports · 2025Article
- Controlling an Industrial Robot Using Stereo 3D Vision Systems with AI Elements.Sensors (Basel, Switzerland) · 2025Article
- A Novel Hybrid Approach for Drowsiness Detection Using EEG Scalograms to Overcome Inter-Subject Variability.Sensors (Basel, Switzerland) · 2025Article
- AIoT-Based Eyelash Extension Durability Evaluation Using LabVIEW Data Analysis.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Convolutional neural networks (CNNs), a type of artificial neural network (ANN) in the deep learning (DL) domain, have gained popularity in several computer vision applications and are attracting research in other fields, including robotic perception. CNNs are developed to autonomously and effectively acquire spatial patterns of characteristics using backpropagation, leveraging an array of elements, including convolutional layers, pooling layers, and fully connected layers. Current reviews predominantly emphasize CNNs' applications in various contexts, neglecting a comprehensive perspective on CNNs and failing to address certain recently presented new ideas, including robotic perception. This review paper presents an overview of the fundamental principles of CNNs and their applications in diverse computer vision tasks for robotic perception while addressing the corresponding challenges and future prospects for the domain of computer vision in improved robotic perception. This paper addresses the history, basic concepts, working principles, applications, and the most important components of CNNs. Understanding the concepts, benefits, and constraints associated with CNNs is crucial for exploiting their possibilities in robotic perception, with the aim of enhancing robotic performance and intelligence.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.