ReviewSensors (Basel, Switzerland)2023
Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
11 citing papers in PubMed.
- Attentional semantic attack for enhancing adversarial samples transferability.Scientific reports · 2026Article
- Optimization of deep learning-based faster R-CNN network for vehicle detection.Scientific reports · 2025Article
- Vehicles detection through wireless sensors networks and optical fiber sensors.Scientific reports · 2025Article
- Fine-grained vehicle recognition under low light conditions using EfficientNet and image enhancement on LiDAR point cloud data.Scientific reports · 2025Article
- Integrated neural network framework for multi-object detection and recognition using UAV imagery.Frontiers in neurorobotics · 2025Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
- Design and Implementation of a Two-Wheeled Vehicle Safe Driving Evaluation System.Sensors (Basel, Switzerland) · 2024Article
- SGK-Net: A Novel Navigation Scene Graph Generation Network.Sensors (Basel, Switzerland) · 2024Article
- High-Magnification Object Tracking with Ultra-Fast View Adjustment and Continuous Autofocus Based on Dynamic-Range Focal Sweep.Sensors (Basel, Switzerland) · 2024Article
- Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles.Sensors (Basel, Switzerland) · 2024Article
- A Long-Tailed Image Classification Method Based on Enhanced Contrastive Visual Language.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.
Indexed as
Identifiers
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.