Introducing the classification of target detection algorithms and their evaluation performance indicators

Object detection is a task in computer vision used to identify and locate objects in images or videos. It plays an important role in applications such as surveillance, autonomous driving, and robotics. Object detection algorithms can be broadly divided into two categories based on the number of times the network uses the same input image.

Single-pass object detection

Single-pass object detection predicts the presence and location of objects in an image in a single pass, improving computational efficiency.

However, single-shot object detection is usually not as accurate as other methods, especially when it comes to detecting small objects. Nonetheless, these algorithms can detect objects in real-time in resource-limited environments.

Twice Object Detection

Object detection typically uses two input images to predict the presence and location of an object. The first detection generates a set of suggestions for potential object locations, while the second detection further refines and filters these suggestions to ultimately produce the most accurate prediction. Although this method is more accurate than single target detection, it also increases the computational cost.

Overall, the choice between single and double object detection depends on the specific requirements and constraints of the application.

Generally, single target detection is more suitable for real-time applications, while two target detection is more suitable for applications where accuracy is more important.

Object detection model performance evaluation metrics

To determine and compare the predictive performance of different object detection models, we need standard quantitative metrics.

The two most common evaluation metrics are the Intersection over Union (IoU) and Average Precision (AP) metrics.

Intersection over Union (IoU)

IoU (Intersection over Union) is a popular metric used to measure positioning accuracy and Compute localization error in object detection models.

To calculate the IoU between the predicted bounding box and the true bounding box, we first obtain the intersection area between the two corresponding bounding boxes of the same object. After this, we calculate the total area covered by the two bounding boxes - also called the "union", and the overlapping area between them called the "intersection".

Intersection divided by Union gives the ratio of overlap to total area, which can be a good estimate of how close the predicted bounding box is to the original bounding box.

Average Precision (AP)

Average Precision (AP) is calculated as the area under the precision versus recall curve for a set of predictions.

Recall is calculated as the ratio of the total predictions made by the model under a category to the total number of existing labels for that category. Precision is the ratio of true positives to the total predictions made by the model.

Recall and precision provide a trade-off represented graphically as a curve by varying the classification threshold. The area under this precision versus recall curve gives us the average precision of the model for each class. The average of this value across all categories is called mean precision (mAP).

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