Boe technology group co., ltd. (20240185590). METHOD FOR TRAINING OBJECT DETECTION MODEL, OBJECT DETECTION METHOD AND APPARATUS simplified abstract
Contents
- 1 METHOD FOR TRAINING OBJECT DETECTION MODEL, OBJECT DETECTION METHOD AND APPARATUS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD FOR TRAINING OBJECT DETECTION MODEL, OBJECT DETECTION METHOD AND APPARATUS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD FOR TRAINING OBJECT DETECTION MODEL, OBJECT DETECTION METHOD AND APPARATUS
Organization Name
boe technology group co., ltd.
Inventor(s)
Guangwei Huang of Beijing (CN)
METHOD FOR TRAINING OBJECT DETECTION MODEL, OBJECT DETECTION METHOD AND APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185590 titled 'METHOD FOR TRAINING OBJECT DETECTION MODEL, OBJECT DETECTION METHOD AND APPARATUS
Simplified Explanation
The abstract describes a method for training an object detection model by obtaining sample image sets, an initial object detection model, and then training the model using the sample image sets.
- Obtaining m sample image sets
- Obtaining an initial object detection model
- Training the initial object detection model using the m sample image sets
- Sample image set includes at least one sample image and object type(s) of object(s) in each sample image
- Object type corresponds to one sample image set, and the m sample image sets correspond to n object types
Potential Applications
This technology can be applied in various fields such as autonomous driving, surveillance systems, and robotics for accurate object detection and recognition.
Problems Solved
This technology solves the problem of training object detection models efficiently and accurately by using sample image sets to improve detection performance.
Benefits
The benefits of this technology include improved object detection accuracy, faster training times, and the ability to adapt to different object types in various environments.
Potential Commercial Applications
Potential commercial applications of this technology include developing advanced security systems, enhancing industrial automation processes, and improving the efficiency of object recognition in retail settings.
Possible Prior Art
One possible prior art for this technology could be the use of convolutional neural networks for object detection in images, which has been a common approach in the field of computer vision.
What are the specific object types that can be detected using this technology?
The specific object types that can be detected using this technology can vary depending on the sample image sets provided during training. The model can be trained to detect a wide range of objects such as vehicles, pedestrians, animals, and various objects in different environments.
How does this technology compare to existing object detection methods in terms of accuracy and efficiency?
This technology aims to improve object detection accuracy and efficiency by utilizing sample image sets to train the model. By providing a diverse range of object types in the training data, the model can learn to detect objects more accurately and efficiently compared to traditional methods.
Original Abstract Submitted
a method for training an object detection model includes: firstly obtaining m sample image sets; then, obtaining an initial object detection model; and finally, training the initial object detection model by using the m sample image sets to obtain the object detection model. a sample image set includes at least one sample image and object type(s) of object(s) in each sample image. an object type corresponds to one sample image set, and the m sample image sets correspond to n object types.
