I have around 1600 images extracted from videos shot at night time. I am labeling each image and trying to be as accurate as I can in assigning bounding boxes. I am labeling vehicles and traffic light/traffic signs. This is very time-consuming, I am wondering if someone have experience or done this before and can advise me on some automated methods of labeling night time images. The objects of interests are usually appear to be quite small. An example labelled image is given below
On the internet, LabelImg is pretty popular. It's a Python program that you can use to automatize drawing of bounding boxes. Alternatively there is HyperLabel, on their website they say it's free so it.s worth to give it a try.
The options available are so many I completely get lost while searching. I suggest you to read this nice review of tools first.
With these tools, labeling 1600 images is not going to be a problem IMHO. Good luck.
If you're drawing bounding boxes around cars, you can use a pretrained model. Hundreds of pretrained networks are trained on the COCO dataset, which has a car category. Then you will get the labels as the output.
Then you can also double check with
LabelImg by iterating through the folder with the keyboard arrows, and adjust the bounding boxes if they are off. But they shouldn't. Cars (and people) are maybe the most cropped category in the world. Use it to your advantage.