Image Detection 전형적인 과정

축구 장면에서 Image Detection 수행 결과

 

축구 장면에서 사람과 공을 찾는 Image Detection 프로젝트를 진행중이다. 관련하여 Image Detection의 전형적인 과정에 대해 아래와 같이 알아보았다.

 

This is the description of the typical algorithmic approach that could be used to find a soccer ball (or any specific object) in an image:

1. Image Preprocessing:

• Resize the Image: The image is resized to a standard dimension to maintain consistency.
• Color Space Conversion: Sometimes, the image is converted to a different color space like grayscale or HSV (Hue, Saturation, Value) to simplify the analysis.

2. Object Detection:

• Using Pre-trained Models: There are pre-trained models like YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN that have been trained on extensive datasets to recognize various objects, including balls.
• Custom Model: A custom model can also be trained for specific objects. In this case, a model could be trained specifically to recognize soccer balls.

3. Region of Interest (ROI):

• Bounding Box: The object detection model will identify the location of the ball and create a bounding box around it.
• Coordinates: The model will provide the coordinates of the bounding box, indicating the location of the ball.

4. Post-processing:

• Filtering: If multiple detections are made, filtering is done based on confidence scores to keep the detection with the highest confidence.
• Display or Output: The final image with the bounding box around the ball is either displayed or the coordinates are outputted for further use.

5. Deep Learning Frameworks:

• TensorFlow and Keras: Popular for developing custom models.
• OpenCV: Widely used for image processing and can be used in conjunction with deep learning models.

6. Real-time Detection:

• Video Stream: If applied to a video stream, the algorithm runs continuously, detecting the ball in each frame.
  The actual process may involve more or fewer steps based on the specific algorithm or model used. A human, like me, reviewing the image, would locate the ball by recognizing its typical shape, size, and pattern, which are characteristic of soccer balls.

 

YOLO, SSD, Faster R-CNN 같은 pre-trained Model들을 사용해 빠른 속도와 사람과 공을 정확하게 찾아내는 능력간  trade-off 의 균형점을 찾는 것이 관건일듯 하다.