Container yards have been facing the increase of freight volume. In order to improve the efficiency of container handling, automatic stations have been established in many terminals. However, current container handling still needs a manual operation to locate container lock holes. Hence, it is inefficient and potential to risk workers’ health under long working hours. This paper presented a hybrid machine vision method to automatically recognize and locate container lock holes. The proposed method extracted the top area of the container from the multiple container areas, and then presented a new modified local sliding window to detect the keyhole region. The algorithm learned the histograms of oriented gradients (HOG) features using a multi-class support vector machine (SVM). Finally, the holes were located using direct least square fitting of ellipses. We carried an experiment under various weather and light conditions including nights and rainy days. The results showed that both the recognition and location accuracy outperformed the state-of-the-art results.

中文翻译：

---

使用改进的局部滑动窗口方法基于视觉的集装箱锁孔检测

集装箱堆场一直面临着货运量的增长。为了提高集装箱装卸的效率，在许多码头都建立了自动站。然而，当前的容器处理仍需要手动操作来定位容器锁定孔。因此，在长时间工作的情况下，效率低下并有可能危害工人的健康。本文提出了一种混合机器视觉方法，用于自动识别和定位容器锁孔。所提出的方法从多个容器区域中提取了容器的顶部区域，然后提出了一种新的改进的局部滑动窗口来检测钥匙孔区域。该算法使用多类支持向量机（SVM）学习了定向梯度（HOG）特征的直方图。最后，使用椭圆的最小二乘法直接定位孔。我们在各种天气和光照条件下（包括夜晚和阴雨天）进行了一项实验。结果表明，识别和定位精度均优于最新结果。