非结构环境下基于HoG与SVM的汽车油箱盖视觉检测方法

为解决在自然场景中进行汽车油箱盖定位的问题，提出一种非结构环境下基于HoG与SVM的汽车油箱盖视觉检测方法。对汽车图像进行预处理并采用多尺度底帽变换提取图像暗细节特征；利用改进的最大熵阈值分割法分割图像；采用连通区域标记法对二值图进行统计，并在原图中确定目标候选区域；采用HoG特征和支持向量机对候选区域进行分类判决，从而定位汽车油箱盖。结果表明：该方法可以准确地检测出油箱盖位置，即使图像存在光照不均匀、汽车覆盖件表面灰尘、细节模糊等情况，也有较好的定位效果。

Visual Detection Method for Automobile Fuel Tank Cover Based on HoG and SVM in Unstructured Environment

In order to solve the problem of automobile fuel tank cover positioning in natural scene, a visual detection method of automobile fuel tank cover based on HoG and SVM in unstructured environment was proposed.The car image was preprocessed and the dark detail features were extracted by using multi-scale bottom-hat transform; the improved maximum entropy threshold segmentation method was used for image segmentation; the connected region labeling method was used to make statistics of the binary image, and the target candidate regions were determined in the original image; the HoG feature and support vector machine were used to classify and decide the candidate area to locate the fuel tank cover.The results show that by using the method, the fuel tank cover position can be accurately detected, even if the image has uneven illumination, dust on the surface of the car coverings and fuzzy details, it also has a good positioning effect.