## **For better user experience, refer to Web official document -> [Object Detection](https://www.paddlepaddle.org.cn/hublist)**

### Object Detection

The goal of an object detection task is to allow a computer to find the locations of all objects when an image or a video frame is given. In addition, the specific category of each object is specified. For the computer, what it "sees" are the digits encoded. It is difficult for the computer to understand the high level semantic concept - whether it is a person or an object in the image or video frame. It is even more difficult to locate where the object exists in the image.

- Recommended Models

| Model Name                                                   | Model Introduction                                           |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [YOLOv3](https://www.paddlepaddle.org.cn/hubdetail?name=yolov3_darknet53_coco2017&en_category=ObjectDetection) | Compared with the original author, the implementation accuracy is increased by 5.9 absolute percents, and the performance is extremely optimized. |
| [Pedestrian detection](https://www.paddlepaddle.org.cn/hubdetail?name=yolov3_darknet53_pedestrian&en_category=ObjectDetection) | Baidu's self-developed model. It is the massive private dataset training, and can be applied to intelligent video surveillance, human behavior analysis, passenger flow statistics system, intelligent transportation and other fields. |
| [Vehicle inspection](https://www.paddlepaddle.org.cn/hubdetail?name=yolov3_darknet53_vehicles&en_category=ObjectDetection) | Baidu's self-developed model. It supports recognition of car, truck, bus, motorbike, tricycle and other vehicle types. |
