A Review of Object Detection Models based on Convolutional Neural Network
arXiv:1905.01614 · doi:10.1007/978-981-15-4288-6_1
Abstract
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.
arXiv comment17 pages, 11 figures, 1 table
References in corpus (12)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- SSD: Single Shot MultiBox Detector
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- Empirical Evaluation of Rectified Activations in Convolutional Network
- Fully Convolutional Networks for Semantic Segmentation
- Feature Pyramid Networks for Object Detection
- Advancements in Image Classification using Convolutional Neural Network
- Aggregated Residual Transformations for Deep Neural Networks
- Single-Shot Refinement Neural Network for Object Detection
- Scalable Object Detection using Deep Neural Networks
- MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
- Object Detection Networks on Convolutional Feature Maps
Cited by in corpus (9)
- Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review
- Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey
- A Comparative Review of Recent Few-Shot Object Detection Algorithms
- Assisting Blind People Using Object Detection with Vocal Feedback
- Deep learning Local Reduced Density Matrices for Many-body Hamiltonian Estimation
- Gallbladder Cancer Detection in Ultrasound Images based on YOLO and Faster R-CNN
- A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
- Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models
- Diabetic Retinopathy detection by retinal image recognizing