YOLO is a state-of-the-art, real-time object detection algorithm. We apply the YOLO algorithm to detect objects in images. Test images are located in the./images/folder.

We will start by loading the required packages into Python. We will be using OpenCV to load our images, matplotlib to plot them, autils module that contains some helper functions, and a modified version of Darknet. YOLO uses Darknet, an open source, deep neural network framework written by the creators of YOLO. The version of Darknet used in this notebook has been modified to work in PyTorch 0.4 and has been simplified because we won't be doing any training. Instead, we will be using a set of pre-trained weights that were trained on the Common Objects in Context (COCO) database.

We will be using the latest version of YOLO, known as YOLOv3.

• yolov3.cfg : contains the network architecture used by YOLOv3 in the /cfg/ folder.
• yolov3.weights : contains the pre-trained weights in the /weights/ directory.
• /data/ directory: contains the coco.names file that has the list of the 80 object classes that the weights were trained to detect.

We start by specifying the location of the files that contain the neural network architecture, the pre-trained weights, and the object classes. We then use Darknet to setup the neural network using the network architecture specified in the cfg_file. We then use the.load_weights() method to load our set of pre-trained weights into the model. Finally, we use the load_class_names() function, from the utils module, to load the 80 object classes.

The neural network used by YOLOv3 consists mainly of convolutional layers, with some shortcut connections and upsample layers.

we load our images using OpenCV's cv2.imread() function. Since, this function loads images as BGR we will convert our images to RGB so we can display them with the correct colors.

The input size of the first layer of the network is 416 x 416 x 3. Since images have different sizes, we have to resize our images to be compatible with the input size of the first layer in the network. We resize our images using OpenCV's cv2.resize() function.

YOLO uses Non-Maximal Suppression (NMS) to only keep the best bounding box. The first step in NMS is to remove all the predicted bounding boxes that have a detection probability that is less than a given NMS threshold. For example, when NMS threshold is set to 0.6: This means that all predicted bounding boxes that have a detection probability less than 0.6 will be removed.

After removing all the predicted bounding boxes that have a low detection probability, the second step in NMS, is to select the bounding boxes with the highest detection probability and eliminate all the bounding boxes whose Intersection Over Union (IOU) value is higher than a given IOU threshold. for examples, if IOU threshold is set to 0.4: this means that all predicted bounding boxes that have an IOU value greater than 0.4 with respect to the best bounding boxes will be removed.

In the utils module the nms function performs the second step of Non-Maximal Suppression, and the boxes_iou function calculates the Intersection over Union of two given bounding boxes.

Once the image has been loaded and resized, and parameters for nms_thresh and iou_thresh need to be set to use YOLO algorithm for object detection in the image. We detect the objects using the detect_objects(m, resized_image, iou_thresh, nms_thresh)function from the utils module. This function takes in the model m returned by Darknet, the resized image, and the NMS and IOU thresholds, and returns the bounding boxes of the objects found.

Each bounding box contains 7 parameters: the coordinates (x, y) of the center of the bounding box, the width w and height h of the bounding box, the confidence detection level, the object class probability, and the object class id. The detect_objects() function also prints out the time it took for the YOLO algorithm to detect the objects in the image and the number of objects detected.

Once we have the bounding boxes of the objects found by YOLO, we can print the class of the objects found and their corresponding object class probability. To do this we use the print_objects() function in the utils module.

Finally, we use the plot_boxes() function to plot the bounding boxes and corresponding object class labels found by YOLO in our image. If the plot_labels flag is set to False the display the bounding boxes is with no labels. This makes it easier to view the bounding boxes if nms_thresh is too low. The plot_boxes()function uses the same color to plot the bounding boxes of the same object class.

YOLOv3 is fast, (under cpu it took 2sec in this project) and this works well even in 30frame/s live video. Even though this model is useful to identify pedestrians, cars, trucks, or motorcycles on the road, however the bouding boxes have their own limits: for example, drawing boxes over vehicles on a curvy road, over forest or trees' or vechicles shadow would be problmatic. It is not easy to convey the true shape of an object. So that bouding boxes can acheive "partial" scene understanding.

• YOLO Object Detection in Tensorflow/Keras
• YOLOv3 Object Detection in Pytorch
• YOLOv3 Object Detection C++
• SSD(Single shot detection)
• Semantic Segmentation for Scene Understanding

• Darknet
• Yolo
• Repository
• YOLOv3 paper
• Yolo9000 2017
• Yolo 2016