# A Review of Object Detection Models based on Convolutional Neural Network

arXiv: https://arxiv.org/abs/1905.01614 · HTML: https://mlsanity.com/paper/1905.01614/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2019.

## Built on by (most cited first, 22 of 22)

- [A Comparative Review of Recent Few-Shot Object Detection Algorithms](https://mlsanity.com/paper/2111.00201/) (2021)
- [Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison](https://mlsanity.com/paper/2203.14205/) (2022)
- [Precise Single-stage Detector](https://mlsanity.com/paper/2210.04252/) (2022)
- [Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications](https://mlsanity.com/paper/2401.01370/) (2023)
- [LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking](https://mlsanity.com/paper/2403.04303/) (2024)
- [Steal Now and Attack Later: Evaluating Robustness of Object Detection against Black-box Adversarial Attacks](https://mlsanity.com/paper/2404.15881/) (2024)
- [Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models](https://mlsanity.com/paper/2505.16647/) (2025)
- [Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing](https://mlsanity.com/paper/2211.01037/) (2022)
- [Gallbladder Cancer Detection in Ultrasound Images based on YOLO and Faster R-CNN](https://mlsanity.com/paper/2404.15129/) (2024)
- [S-E Pipeline: A Vision Transformer (ViT) based Resilient Classification Pipeline for Medical Imaging Against Adversarial Attacks](https://mlsanity.com/paper/2407.17587/) (2024)
- [Synthetic Dataset Generation for Autonomous Mobile Robots Using 3D Gaussian Splatting for Vision Training](https://mlsanity.com/paper/2506.05092/) (2025)
- [Detection and Initial Assessment of Lunar Landing Sites Using Neural Networks](https://mlsanity.com/paper/2207.11413/) (2022)
- [A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks](https://mlsanity.com/paper/2207.13551/) (2022)
- [Can the Query-based Object Detector Be Designed with Fewer Stages?](https://mlsanity.com/paper/2309.16306/) (2023)
- [How To Effectively Train An Ensemble Of Faster R-CNN Object Detectors To Quantify Uncertainty](https://mlsanity.com/paper/2310.04829/) (2023)
- [Assisting Blind People Using Object Detection with Vocal Feedback](https://mlsanity.com/paper/2401.01362/) (2023)
- [Bottleneck-based Encoder-decoder ARchitecture (BEAR) for Learning Unbiased Consumer-to-Consumer Image Representations](https://mlsanity.com/paper/2409.06187/) (2024)
- [LiCAR: pseudo-RGB LiDAR image for CAR segmentation](https://mlsanity.com/paper/2501.13960/) (2025)
- [DRiVE: Dynamic Recognition in VEhicles using snnTorch](https://mlsanity.com/paper/2502.10421/) (2025)
- [Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study](https://mlsanity.com/paper/2505.11424/) (2025)
- [YOLOBirDrone: Dataset for Bird vs Drone Detection and Classification and a YOLO based enhanced learning architecture](https://mlsanity.com/paper/2601.08319/) (2026)
- [Squeeze-Aware-YOLO: Early Channel Recalibration and Multi-Scale Fusion for Real-Time Traffic Sign Detection](https://mlsanity.com/paper/2610.15604/) (2026)

## Abstract (arXiv)

Convolutional Neural Network (CNN) has become the state-of-the-art for object detection in image task. In this chapter, we have explained different state-of-the-art CNN based object detection models. We have made this review with categorization those detection models according to two different approaches: two-stage approach and one-stage approach. Through this chapter, it has shown advancements in object detection models from R-CNN to latest RefineDet. It has also discussed the model description and training details of each model. Here, we have also drawn a comparison among those models.