目标检测中常见且标准化的评价指标主要围绕 定位精度（Localization）、分类正确性（Classification） 和 整体检测性能 三个方面。
参考链接：目标检测的评价指标（七个）
机器学习分类中的四大指标：TP、TN、FP、FN

一、样本

首先说明一下样本的概念，样本分为正样本和负样本。正样本即为需要被检测出的物体，负样本是不需要被检测出的物体。由此可衍生出真正例（TP）、假正例（FP）、假负例（FN）和真负例（TN）四个概念

真正例 TP

实际为正类的样本被正确地预测为正类的数量

假正例 FP

即负样本被检测为正样本的数量，俗称误报

假负例 FN

实际为正类的样本被错误地预测为负类，也称漏报

真负例 TN

实际为负类的样本被正确地预测为负类，无法计算

二、交并比（Intersection over Union， IoU）

用于判断预测框是否命中，顾名思义交并比的意思就是交集区域和并集区域的比值。通常会对IOU设置阈值进行预测标签筛选，常见阈值：IoU ≥ 0.5（VOC）、IoU ≥ 0.75（更严格）、 0.5:0.95（COCO，多阈值平均）
预测框预测框

三、 Precision（精确率）

模型预测为“目标”的框中，有多少是真的，关注误检
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四、Recall（召回率）

GT 中有多少目标被成功检测，关注漏检
​

五、Precision–Recall 曲线与 AP

PR Curve（Precision–Recall 曲线）

通过调整 confidence score 阈值得到不同的预测结果，对每一个阈值： 计算对应的 Precision 和 Recall并画在坐标系中（横轴：Recall 纵轴：Precision）
PR 曲线本质上反映的是： 曲线越靠右上角 → 模型越好 曲线越平滑、越高 → 模型稳定性越好

AP（Average Precision）

即 PR 曲线下的面积

mAP(mean Average Precision)

即多类别平均，计算出每个类别的AP以后，对于所有类别的AP取均值就得到mAP了

参考代码

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#计算recall, precision和AP
class_recs = {}
    npos = 0
    for imagename in imagenames:
        R = [obj for obj in recs[imagename] if obj['name'] == classname] 
                bbox = np.array([x['bbox'] for x in R])
        difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
        det = [False] * len(R) #这个值是用来判断是否重复检测的
        npos = npos + sum(~difficult)
        class_recs[imagename] = {'bbox': bbox,
                                 'difficult': difficult,
                                 'det': det}

    # read dets
    detfile = detpath.format(classname)
    with open(detfile, 'r') as f:
        lines = f.readlines()

    splitlines = [x.strip().split(' ') for x in lines]
    image_ids = [x[0] for x in splitlines]
    confidence = np.array([float(x[1]) for x in splitlines])
    BB = np.array([[float(z) for z in x[2:]] for x in splitlines])

    # sort by confidence 
    sorted_ind = np.argsort(-confidence)
    BB = BB[sorted_ind, :]
    image_ids = [image_ids[x] for x in sorted_ind]

    # go down dets and mark TPs and FPs
    nd = len(image_ids) 
    tp = np.zeros(nd)
    fp = np.zeros(nd)
    for d in range(nd):
        R = class_recs[image_ids[d]]
        bb = BB[d, :].astype(float)
        ovmax = -np.inf 
        BBGT = R['bbox'].astype(float)

        if BBGT.size > 0:
            # compute overlaps
            # intersection
            ixmin = np.maximum(BBGT[:, 0], bb[0])
            iymin = np.maximum(BBGT[:, 1], bb[1])
            ixmax = np.minimum(BBGT[:, 2], bb[2])
            iymax = np.minimum(BBGT[:, 3], bb[3])
            iw = np.maximum(ixmax - ixmin + 1., 0.)
            ih = np.maximum(iymax - iymin + 1., 0.)
            inters = iw * ih

            # union
            uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
                   (BBGT[:, 2] - BBGT[:, 0] + 1.) *
                   (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)

            overlaps = inters / uni
            ovmax = np.max(overlaps)
            jmax = np.argmax(overlaps)

        if ovmax > ovthresh:
            if not R['difficult'][jmax]: 
                if not R['det'][jmax]:
                    tp[d] = 1.
                    R['det'][jmax] = 1 #判断是否重复检测，检测过一次以后，值就从False变为1了
                else:
                    fp[d] = 1.
        else:
            fp[d] = 1.

    # compute precision recall
    fp = np.cumsum(fp) 
    tp = np.cumsum(tp)
    rec = tp / float(npos)
    # avoid divide by zero in case the first detection matches a difficult
    # ground truth
    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
    ap = voc_ap(rec, prec, use_07_metric)

    return rec, prec, ap
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def voc_ap(rec, prec, use_07_metric=False):
    """Compute VOC AP given precision and recall. If use_07_metric is true, uses
    the VOC 07 11-point method (default:False).
    """
    if use_07_metric:
        # 11 point metric
        ap = 0.
        for t in np.arange(0., 1.1, 0.1):
            if np.sum(rec >= t) == 0:
                p = 0 
            else:
                p = np.max(prec[rec >= t])
            ap = ap + p / 11.
    else:
        # correct AP calculation
        # first append sentinel values at the end
        mrec = np.concatenate(([0.], rec, [1.]))
        mpre = np.concatenate(([0.], prec, [0.]))

        # compute the precision envelope
        for i in range(mpre.size - 1, 0, -1):
            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
        
        i = np.where(mrec[1:] != mrec[:-1])[0]

        # and sum (\Delta recall) * prec
        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) #计算面积
    return ap
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def mAP():
    detpath,annopath,imagesetfile,cachedir,class_path = get_dir('kitti')
    ovthresh=0.3,
    use_07_metric=False
    rec = 0; prec = 0; mAP = 0
    class_list = get_classlist(class_path)
    for classname in class_list:
        rec, prec, ap = voc_eval(detpath,
                                 annopath,
                                 imagesetfile,
                                 classname,
                                 cachedir,
                                 ovthresh=0.5,
                                 use_07_metric=False,
                                 kitti=True)
        print('on {}, the ap is {}, recall is {}, precision is {}'.format(classname, ap, rec[-1], prec[-1]))
        mAP += ap   
    mAP = float(mAP) / len(class_list)
    return mAP