通过机器学习从图像计算车辆数量 [英] Counting the number of vehicles from an image with machine learning
问题描述
我是机器学习的新手.我有一项任务是使用机器学习概念从图像中查找车辆总数.我正在使用神经网络.我的最坏情况的图像在这里给出.
I am new to machine learning. I got a task to find the total number of vehicles from an image using machine learning concept. I am using neural network. My image of worst case is given here.
我需要从该图像中找到汽车的总数.我的想法是将大图像切成小块图像，并训练网络以从小块中计算出车辆的数量.每个补丁的计数都小于5.然后在处理新图像时，我可以利用滑动窗口来获取车辆总数.
I need to find the total number of cars from this image. My idea is to cut this big image into small patches of image and train the network to count the vehicles from the small patches. Each patch will be having count less than 5. Then in the processing of new image, I could make use of a sliding window to get the total count of vehicles.
我只想知道这个想法是否可行，或者我应该去进行特征提取和训练具有这些特征的神经网络.如果可能，数据集和训练是否有条件.
I just want to know whether this idea is possible or not OR should I go for feature extraction and training neural network with those features. If possible, whether there is any conditions for the dataset and training.
推荐答案
您要查找的内容称为对象检测.起点可以是用于对象检测的深度神经网络或用于精确对象检测和分割的基于区域的卷积网络.
What you are looking for is called object detection. A starting point can be Deep Neural Networks for Object Detection or Region-based Convolutional Networks for Accurate Object Detection and Segmentation.
一个类似但困难得多的任务是实例细分.我在这方面看到的最新论文之一是用于实例级语义标记的像素级编码和深度分层.
A similar, but much more difficult task is instance segmentation. One of the latest papers I've seen in this area is Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling.
实例分段可能是Computer Vision中最难的任务.如果您不熟悉机器学习/计算机视觉，则可能首先要进行图像分类.如果要进入实例分割的方向，则应继续进行语义分割，然后再进行实例分割.
Instance segmentation is probably the hardest tasks in Computer Vision. When you're new to machine learning / computer vision, you might first want to do image classification. If you want to go into the direction of instance segmentation, then you should continue with semantic segmentation and then instance segmentation.
一种简单的滑动窗口方法，您只能预测汽车"/没有汽车"，因为在图像中汽车没有被任何没有汽车"隔开.
A simple sliding window approach, where you only predict "car" / "no car" will not work, because in the image the cars are not separated by any "no car".
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