object detection物体检测基本概念

最新推荐文章于 2024-04-12 20:44:30 发布

转载最新推荐文章于 2024-04-12 20:44:30 发布 · 70 阅读

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原文链接：http://www.cnblogs.com/wordchao/p/8133808.html

本文详细介绍了在目标检测任务中评估模型性能的两个关键指标：精确率（Precision）和召回率（Recall）。精确率衡量了所有被检测为正例的对象中真正例的比例，而召回率则反映了所有实际为正例的对象中被正确检测出来的比例。对于多类别检测器，这两个指标分别以向量或单元数组的形式给出，每个元素对应一个特定的类别。

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1.precision-recall

https://cn.mathworks.com/help/vision/ref/evaluatedetectionprecision.html?requestedDomain=www.mathworks.com#outputarg_precision

`averagePrecision` — Average precision
numeric scalar | vector

Average precision over all the detection results, returned as a numeric scalar or vector. Precision is a ratio of true positive instances to all positive instances of objects in the detector, based on the ground truth. For a multiclass detector, the average precision is a vector of average precision scores for each object class.

`recall` — Recall values from each detection
vector of numeric scalars | cell array

Recall values from each detection, returned as a vector of numeric scalars or as a cell array. Recall is a ratio of true positive instances to the sum of true positives and false negatives in the detector, based on the ground truth. For a multiclass detector, recalland precision are cell arrays, where each cell contains the data points for each object class.

`precision` — Precision values from each detection
vector of numeric scalars | cell array

Precision values from each detection, returned as a vector of numeric scalars or as a cell array. Precision is a ratio of true positive instances to all positive instances of objects in the detector, based on the ground truth. For a multi-class detector, recall and precision are cell arrays, where each cell contains the data points for each object class.

转载于:https://www.cnblogs.com/wordchao/p/8133808.html