Deep Learning for Object Detection and Grasping: A Survey

Citations Over Time

Abstract

Detecting and grasping objects in unstructured environments is an important yet difficult task. Fortunately, the breakthroughs from deep convolutional networks stimulate the development of object detection and grasping. The survey aims to serve as a comparison for region-based and region-free detection framework based on deep learning, and supplies the latest research results of object grasping with deep learning. Firstly, we briefly analyze the object detection and grasping. Then, the representative object detection methods based on deep learning are overviewed. Thirdly, we introduce the application of convolutional neural networks in object grasping. Finally, the potential trends in object detection and grasping based on deep learning are discussed.

Related Papers

• → Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning(2019)60 cited
• → Automated Fruit Classification Using Deep Convolutional Neural Network(2020)4 cited
• → Implementing convolutional neural network model for prediction in medical imaging(2022)6 cited
• → Leaf Features Extraction for Plant Classification using CNN(2021)7 cited
• → Deep Convolution Neural Network for RBC Images(2022)2 cited