Object Detection and Avoidance using Computer Vision
By: Bhushan Lokhande
Tags: Pune, Machine Learning, Computer Science, Computer Vision, Object Detection

Vaishnavi Jahagirdar(50), Amruta Lhyayakar(65), Bhushan Lokhande(67), Ganesh Maher(73), Vishwakarma Institute of Technology, Pune,India. What is object detection and avoidance?

Object Detection is a computer vision technology that is used to detect objects present in front of a camera such as a person, car, pole, road. Object detection in the future is going to be a day-to-day part of our life as we will be using object detection in live surveillance cameras, road traffic monitoring, self-driving cars, autonomous robots, and the list goes on..

Object Avoidance is the next step after object detection. Object avoidance means after the specific object is detected using computer vision algorithm we give certain outputs to self-driving cars, autonomous robots, humanoid robots which contain defined actions like stop, decelerate, dodge, or strike. Mostly we know them in the name of Tesla AutoPilot Cars, Boston Dynamics Robot Dog.

Object Detection using YOLOv3

Implemented object detection using YOLO technique

YOLOv3 is the latest variant of a popular object detection algorithm YOLO — You Only Look Once. The published model recognizes 80 different objects in images and videos, but most importantly it is super fast and nearly as accurate as Single Shot MultiBox (SSD). Starting with OpenCV 3.4.2, you can easily use YOLOv3 models in your own OpenCV application. We can think of an object detector as a combination of an object locator and an object recognizer.

In traditional computer vision approaches, a sliding window was used to look for objects at different locations and scales. Because this was such an expensive operation, the aspect ratio of the object was usually assumed to be fixed. Early Deep Learning based object detection algorithms like the R-CNN and Fast R-CNN used a method called Selective Search to narrow down the number of bounding boxes that the algorithm had to test.

YOLO on the other hand approaches the object detection problem in a completely different way. It forwards the whole image only once through the network. SSD is another object detection algorithm that forwards the image once through a deep learning network, but YOLOv3 is much faster than SSD while achieving very comparable accuracy. YOLOv3 gives faster than real-time results on M40, TitanX, or 1080 Ti GPUs.

Let's see how YOLO detects the objects in a given image.

First, it divides the image into a 13×13 grid of cells. The size of these 169 cells varies depending on the size of the input. For a 416×416 input size that we used in our experiments, the cell size was 32×32. Each cell is then responsible for predicting a number of boxes in the image. For each bounding box, the network also predicts the confidence that the bounding box actually encloses an object and the probability of the enclosed object being a particular class. Most of these bounding boxes are eliminated because their confidence is low or because they are enclosing the same object as another bounding box with a very high confidence score. This technique is called non-maximum suppression.

Object Avoidance

Object avoidance is the next step after object detection. As the specific object is detected motors present in self-driving cars or robots are commanded to stop decelerate or take actions according to the output of the microprocessor which is following the algorithm designed according to the application. Different applications have different tests to follow to achieve maximum accuracy.

Detection of the object in front of the camera

a motor car safety system designed to prevent or reduce the severity of a collision. In its basic form, a forward-collision warning system monitors a vehicle’s speed, the speed of the vehicle in front of it, and the distance between the vehicles, so that it can provide a warning to the driver if the vehicles get too close, potentially helping to avoid a crash. Various technologies and sensors that are used include radar(all-weather) and sometimes laser(lidar) and cameras (employing object recognition) to detect an imminent crash. GPS sensors can detect fixed dangers such as approaching stop signs through a location database. Pedestrian detection can also be a feature of these types of systems.

Pedestrian detection in cars

One the most important task for avoidance techniques is to detect pedestrian and stop self driving cars as it can have serious consequences if the algorithm or motors fails detecting or giving output.

Once an impending collision is detected, these systems provide a warning to the driver. When the collision becomes imminent, they can take action autonomously without any driver input (by braking or steering or both). Collision avoidance by braking is appropriate at low vehicle speeds (e.g. below 50 km/h (31 mph)), while collision avoidance by steering may be more appropriate at higher vehicle speeds if lanes are clear.[10] Cars with collision avoidance may also be equipped with adaptive cruise control, using the same forward-looking sensors.

According to Euro NCAP, AEB has three characteristics:

Autonomous: the system acts independently of the driver to avoid or mitigate the accident.

Emergency: the system will intervene only in a critical situation.

Braking: the system tries to avoid the accident by applying the brakes.

Time-to-collision could be a way to choose which avoidance method (braking or steering) is most appropriate.

Some Key Applications:-

Self Driving Cars-Buses Autonomous Robots Autonomous Drones Industrial Robots Small Scale Transport Bots.

Conclusion

Object detection is a key ability for most computer and robot vision system. Although great progress has been observed in the last years, and some existing techniques are now part of many consumer electronics (e.g., face detection for auto-focus in smartphones) or have been integrated into assistant driving technologies, we are still far from achieving human-level performance, in particular in terms of open-world learning. It should be noted that object detection has not been used much in many areas where it could be of great help.