YOLO-object-detection-with-OpenCV

Object detection using YOLO object detector

Detect objects in both images and video streams using Deep Learning, OpenCV, and Python.

I’ll be using YOLOv3 in this project, in particular, YOLO trained on the COCO dataset.

The COCO dataset consists of 80 labels, including, but not limited to:

People
Bicycles
Cars and trucks
Airplanes
Stop signs and fire hydrants
Animals, including cats, dogs, birds, horses, cows, and sheep, to name a few
Kitchen and dining objects, such as wine glasses, cups, forks, knives, spoons, etc. …and much more!

You can find a full list of what YOLO trained on the COCO dataset can detect using this link.

yolo-coco : The YOLOv3 object detector pre-trained (on the COCO dataset) model files. These were trained by the Darknet team.

YOLO object detection in images

Installation

pip install numpy
pip install opencv-python

To Run the project

python yolo.py --image images/baggage_claim.jpg

Screenshots

Here you can see that YOLO has not only detected each person in the input image, but also the suitcases as well!

Furthermore, if you take a look at the right corner of the image you’ll see that YOLO has also detected the handbag on the lady’s shoulder.

YOLO is able to correctly detect each of the players on the pitch, including the soccer ball itself. Notice the person in the background who is detected despite the area being highly blurred and partially obscured.

YOLO object detection in video streams

Installation

pip install numpy
pip install opencv-python

To Run the project

python yolo_video.py --input videos/airport.mp4 --output output/airport_output.avi --yolo yolo-coco

Screenshots

In the video/GIF, you can see not only the vehicles being detected, but people, as well as the traffic lights, are detected too!

The YOLO object detector is performing quite well here.

Limitation:

Arguably the largest limitation and drawback of the YOLO object detector is that:

It does not always handle small objects well
It especially does not handle objects grouped close together
The reason for this limitation is due to the YOLO algorithm itself:

The YOLO object detector divides an input image into an SxS grid where each cell in the grid predicts only a single object. If there exist multiple, small objects in a single cell then YOLO will be unable to detect them, ultimately leading to missed object detections. Therefore, if you know your dataset consists of many small objects grouped close together then you should not use the YOLO object detector.

In terms of small objects, Faster R-CNN tends to work the best; however, it’s also the slowest.

SSDs can also be used here; however, SSDs can also struggle with smaller objects (but not as much as YOLO).

SSDs often give a nice tradeoff in terms of speed and accuracy as well.

Real-time object detection with deep learning and OpenCV

Installation

pip install numpy
pip install opencv-python
pip install imutils

To Run the project

python real_time_object_detection.py

Screenshots

Notice how our deep learning object detector can detect not only a person, but also the sofa and the chair next to person — all in real-time!

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