解決圖像目標檢測兩框重疊問題

1 問題現象

使用yolo v3 等目標檢測模型訓練自己數據集，預測圖片時出現問題： 兩框重疊，如下圖所示：對於同樣一輛汽車，模型反覆的標記。

2 解決辦法

解決辦法就是：非極大值抑制(Non-Maximum Suppression)

3 Non-Maximum Suppression 原理

3.1 什麼是非極大值抑制

非極大值抑制，簡稱爲NMS算法，英文爲Non-Maximum Suppression。其思想是搜素局部最大值，抑制極大值。NMS算法在不同應用中的具體實現不太一樣，但思想是一樣的。非極大值抑制，在計算機視覺任務中得到了廣泛的應用，例如邊緣檢測、人臉檢測、目標檢測（DPM，YOLO，SSD，Faster R-CNN）等。

3.2 爲什麼要用非極大值抑制

以目標檢測爲例：目標檢測的過程中在同一目標的位置上會產生大量的候選框，這些候選框相互之間可能會有重疊，此時我們需要利用非極大值抑制找到最佳的目標邊界框，消除冗餘的邊界框。Demo如下圖：

左圖是人臉檢測的候選框結果，每個邊界框有一個置信度得分(confidence score)，如果不使用非極大值抑制，就會有多個候選框出現。右圖是使用非極大值抑制之後的結果，符合我們人臉檢測的預期結果。

3.3 如何使用非極大值抑制

前提：目標邊界框列表及其對應的置信度得分列表，設定閾值，閾值用來刪除重疊較大的邊界框。
IoU：intersection-over-union，即兩個邊界框的交集部分除以它們的並集。

非極大值抑制的流程如下：

1. 根據置信度得分進行排序
2. 選擇置信度最高的比邊界框添加到最終輸出列表中，將其從邊界框列表中刪除
3. 計算所有邊界框的面積
4. 計算置信度最高的邊界框與其它候選框的IoU。
5. 刪除IoU大於閾值的邊界框
6. 重複上述過程，直至邊界框列表爲空。

Python代碼如下：

#!/usr/bin/env python
# _*_ coding: utf-8 _*_


import cv2
import numpy as np


"""
    Non-max Suppression Algorithm

    @param list  Object candidate bounding boxes
    @param list  Confidence score of bounding boxes
    @param float IoU threshold

    @return Rest boxes after nms operation
"""
def nms(bounding_boxes, confidence_score, threshold):
    # If no bounding boxes, return empty list
    if len(bounding_boxes) == 0:
        return [], []

    # Bounding boxes
    boxes = np.array(bounding_boxes)

    # coordinates of bounding boxes
    start_x = boxes[:, 0]
    start_y = boxes[:, 1]
    end_x = boxes[:, 2]
    end_y = boxes[:, 3]

    # Confidence scores of bounding boxes
    score = np.array(confidence_score)

    # Picked bounding boxes
    picked_boxes = []
    picked_score = []

    # Compute areas of bounding boxes
    areas = (end_x - start_x + 1) * (end_y - start_y + 1)

    # Sort by confidence score of bounding boxes
    order = np.argsort(score)

    # Iterate bounding boxes
    while order.size > 0:
        # The index of largest confidence score
        index = order[-1]

        # Pick the bounding box with largest confidence score
        picked_boxes.append(bounding_boxes[index])
        picked_score.append(confidence_score[index])

        # Compute ordinates of intersection-over-union(IOU)
        x1 = np.maximum(start_x[index], start_x[order[:-1]])
        x2 = np.minimum(end_x[index], end_x[order[:-1]])
        y1 = np.maximum(start_y[index], start_y[order[:-1]])
        y2 = np.minimum(end_y[index], end_y[order[:-1]])

        # Compute areas of intersection-over-union
        w = np.maximum(0.0, x2 - x1 + 1)
        h = np.maximum(0.0, y2 - y1 + 1)
        intersection = w * h

        # Compute the ratio between intersection and union
        ratio = intersection / (areas[index] + areas[order[:-1]] - intersection)

        left = np.where(ratio < threshold)
        order = order[left]

    return picked_boxes, picked_score


# Image name
image_name = 'nms.jpg'

# Bounding boxes
bounding_boxes = [(187, 82, 337, 317), (150, 67, 305, 282), (246, 121, 368, 304)]
confidence_score = [0.9, 0.75, 0.8]

# Read image
image = cv2.imread(image_name)

# Copy image as original
org = image.copy()

# Draw parameters
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 1
thickness = 2

# IoU threshold
threshold = 0.4

# Draw bounding boxes and confidence score
for (start_x, start_y, end_x, end_y), confidence in zip(bounding_boxes, confidence_score):
    (w, h), baseline = cv2.getTextSize(str(confidence), font, font_scale, thickness)
    cv2.rectangle(org, (start_x, start_y - (2 * baseline + 5)), (start_x + w, start_y), (0, 255, 255), -1)
    cv2.rectangle(org, (start_x, start_y), (end_x, end_y), (0, 255, 255), 2)
    cv2.putText(org, str(confidence), (start_x, start_y), font, font_scale, (0, 0, 0), thickness)

# Run non-max suppression algorithm
picked_boxes, picked_score = nms(bounding_boxes, confidence_score, threshold)

# Draw bounding boxes and confidence score after non-maximum supression
for (start_x, start_y, end_x, end_y), confidence in zip(picked_boxes, picked_score):
    (w, h), baseline = cv2.getTextSize(str(confidence), font, font_scale, thickness)
    cv2.rectangle(image, (start_x, start_y - (2 * baseline + 5)), (start_x + w, start_y), (0, 255, 255), -1)
    cv2.rectangle(image, (start_x, start_y), (end_x, end_y), (0, 255, 255), 2)
    cv2.putText(image, str(confidence), (start_x, start_y), font, font_scale, (0, 0, 0), thickness)

# Show image
cv2.imshow('Original', org)
cv2.imshow('NMS', image)
cv2.waitKey(0)

3.4 效果

具體解決辦法就是減小 IoU threshold （IoU 閾值）

IoU閾值爲0.6的時候：

IoU閾值爲0.4的時候：

4 參考資料：

• 《非極大值抑制(Non-Maximum Suppression)》：https://zhuanlan.zhihu.com/p/37489043
• 《訓練自己數據集，預測圖片時出現問題》https://github.com/qqwweee/keras-yolo3/issues/354