计算机工程与设计
計算機工程與設計
계산궤공정여설계
COMPUTER ENGINEERING AND DESIGN
2014年
7期
2531-2535
,共5页
弯道检测%机器学习%塔式梯度直方图%支持向量机%恶劣天气
彎道檢測%機器學習%塔式梯度直方圖%支持嚮量機%噁劣天氣
만도검측%궤기학습%탑식제도직방도%지지향량궤%악렬천기
curve detection%machine learning%PHOG%SVM%bad weather
为了能准确地判断弯道情况，提出了一种基于机器学习的弯道自动检测方法。提取道路弯道训练图像的塔式梯度直方图（PHOG）特征，利用支持向量机对提取的特征进行训练形成分类模型；利用该模型和弯道测试图像的塔式梯度直方图特征对道路弯道情况进行预测。测试结果表明，该方法能够在理想天气和不同程度的恶劣天气下准确判断左弯道和右弯道，对于不同弯度的左右弯道，其平均分类准确率达90％以上。
為瞭能準確地判斷彎道情況，提齣瞭一種基于機器學習的彎道自動檢測方法。提取道路彎道訓練圖像的塔式梯度直方圖（PHOG）特徵，利用支持嚮量機對提取的特徵進行訓練形成分類模型；利用該模型和彎道測試圖像的塔式梯度直方圖特徵對道路彎道情況進行預測。測試結果錶明，該方法能夠在理想天氣和不同程度的噁劣天氣下準確判斷左彎道和右彎道，對于不同彎度的左右彎道，其平均分類準確率達90％以上。
위료능준학지판단만도정황，제출료일충기우궤기학습적만도자동검측방법。제취도로만도훈련도상적탑식제도직방도（PHOG）특정，이용지지향량궤대제취적특정진행훈련형성분류모형；이용해모형화만도측시도상적탑식제도직방도특정대도로만도정황진행예측。측시결과표명，해방법능구재이상천기화불동정도적악렬천기하준학판단좌만도화우만도，대우불동만도적좌우만도，기평균분류준학솔체90％이상。
To accurately judge the curves,a method of curve automatic detection based on machine learning was put forward. Firstly PHOG feature of curve images including training and testing images was extracted.Secondly SVM was employed in trai-ning a classification model with the PHOG feature of training images,and then the classification model was used to predict the road curves with PHOG feature of testing images.This method could accurately determine the left and right curves,while its average classification accuracy of the left and right for different camber curves reached above 90%in the context of ideal weather and different degrees of bad weather.
