Abstract. As optical distribution frames (ODFs) carry increasing amounts of data, issues relating to their port management are becoming crucial. Image backhaul inspection has been widely accepted as an effective technology to monitor the occupancy status of ODF ports. However, the massive amount of on-site images requires a large team of off-site technologists to manually identify the occupancy status of each ODF port, which is time-consuming. We employ the you only look once version 3 (YOLOv3) network to automatically recognize ODF port occupancy. The YOLOv3 is a state-of-the-art convolutional neural network that has been shown to be very efficient for object detection in terms of processing speed and accuracy for the common objects in context. To accommodate ODF images with densely arranged small objects, high resolutions, closely spaced adjacent ports, and occlusion, we modified the original YOLOv3 with four-scale feature fusion, anchor box dimension clustering, and soft nonmaximum suppression filtering. Experiments showed a 7.38% increase in the original YOLOv3 detection accuracy rate of 91.45%. The new method can update the image backhaul inspection to automatically realize port resource management. The number of required port management technologists is considerably reduced, and the accuracy of port resources is increased, resulting in significant network investment savings.

中文翻译：

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基于卷积神经的光配线架端口占用状态检测算法

摘要。随着光配线架 (ODF) 承载越来越多的数据，与其端口管理相关的问题变得至关重要。图像回程检测作为一种有效的ODF端口占用状态监控技术已被广泛接受。然而，大量的现场图像需要大量的异地技术人员手动识别每个ODF端口的占用状态，非常耗时。我们使用你只看一次版本 3 (YOLOv3) 网络来自动识别 ODF 端口占用。YOLOv3 是最先进的卷积神经网络，已被证明在处理上下文中常见对象的处理速度和准确性方面非常有效。为了容纳具有密集排列的小物体、高分辨率的 ODF 图像，紧密间隔的相邻端口和遮挡，我们使用四尺度特征融合、锚框维度聚类和软非最大抑制过滤修改了原始 YOLOv3。实验表明，原YOLOv3检测准确率91.45%提高了7.38%。新方法可以更新镜像回传检测，自动实现端口资源管理。所需的港口管理技术人员数量大幅减少，提高了港口资源的准确性，从而显着节省了网络投资。新方法可以更新镜像回传检测，自动实现端口资源管理。大大减少了所需的港口管理技术人员的数量，提高了港口资源的准确性，从而显着节省了网络投资。新方法可以更新镜像回传检测，自动实现端口资源管理。大大减少了所需的港口管理技术人员的数量，提高了港口资源的准确性，从而显着节省了网络投资。