Object detection is a broad area of computer vision that can be used to create autonomous systems that guide agents through environments, whether they are robots carrying out tasks or detecting objects, but this requires crossroads with other fields. However, anomaly detection locating objects within images, face image detection, and various other applications of detecting objects can be done without cutting into other fields.
Object detection is not only formalized in image detection, but substantially so because most of the new developments are generally done by individual experimenters, maintainers, and inventors rather than by large libraries and frameworks.
Uses cases and application
1. Crowd counting
Another useful application of object detection is crowd counting. Object detection can enable businesses and municipalities to better effectively track diverse types of traffic, whether on foot or in automobiles, in heavily populated areas like theme parks, malls, and city squares. Crowd counting and object detection technology can be used at malls to count the number of people entering and exiting. Automatic helmet recognition can be handled using deep learning and computer vision-based methods because it is essentially an item detection problem. Deep learning and its applications in computer vision have made significant advancements due to their computational approach and accuracy in the realm of object detection. Theme parks can employ object detection to monitor occupancy and availability metrics to assess how well rides are being occupied. The number of seats available on various rides can be determined using real-time video, and computer vision models can attempt to reroute traffic away from overcrowded rides and towards undercrowded ones.
2. Self-driving cars
The effectiveness of autonomous vehicle systems depends on real-time car detection models. These systems need to be able to identify, locate, and track surrounding objects in order to navigate the world safely and effectively.
The current effort to make self-driving cars a reality is supported by the fact that, despite the fact that tasks like picture segmentation can be applied to autonomous vehicles, object identification still remains a fundamental problem.
How does self-driving work?
Systems for self-driving cars are powered by AI. Self-driving car developers integrate enormous amounts of data from image recognition systems with machine learning and neural networks to produce systems that can operate autonomously. The patterns that the neural networks identify in the data are then provided to the machine learning algorithms. One of the data sources the neural network uses to teach itself to recognize objects like traffic lights, trees, curbs, pedestrians, street signs, and other features of a given driving environment are images taken by self-driving car cameras.
Python implementation: YOLOv5 algorithm
You only look once, or YOLO, as it is more often known, was a breakthrough in the field of object detection. This method was the first to handle object detection as a regression issue. By using this model, you may identify the presence and location of items in an image with only one glance.
Yolo, in contrast to the two-stage detector approach, employs a single neural network that predicts class probabilities and bounding box coordinates from an entire image in a single pass. Since the detection pipeline is essentially one network think of it as an image classification network it can be optimized from beginning to end.
The fundamental YOLO model predicts images at 45 FPS (frames per second) when benchmarked on a Titan X GPU because the network is made to train in an end-to-end manner similar to image classification.
It is even more exceptional because YOLO attained 63.4 mAP (mean average precision), more than twice as much as the other real-time detectors. While YOLO predicts fewer false positives in the background than other cutting-edge models like Faster-RCNN, it generates more localization errors (false negatives), particularly for small objects.
Code for object detection
def get_detections(img,net):
image = img.copy()
row, col, d = image.shape
max_rc = max(row,col)
input_image = np.zeros((max_rc,max_rc,3),dtype=np.uint8)
input_image[0:row,0:col] = image
blob=cv2.dnn.blobFromImage(input_image,1/255,(INPUT_WIDTH,INPUT_HEIGHT),
swapRB = True,crop=False)
net.setInput(blob)
preds = net.forward()
detections = preds[0]
return input_image, detections
def non_maximum_supression(input_image,detections):
boxes = []
confidences = []
index = []
image_w, image_h = input_image.shape[:2]
x_factor = image_w/INPUT_WIDTH
y_factor = image_h/INPUT_HEIGHT
for i in range(len(detections)):
row = detections[i]
confidence = row[4] # confidence of detecting
if confidence > 0.4:
class_score = row[5] # probability score
if class_score > 0.25:q
cx, cy , w, h = row[0:4]
left = int((cx - 0.5*w)*x_factor)
top = int((cy-0.5*h)*y_factor)
width = int(w*x_factor)
height = int(h*y_factor)
box = np.array([left,top,width,height])
confidences.append(confidence)
boxes.append(box)
boxes_np = np.array(boxes).tolist()
confidences_np = np.array(confidences).tolist()
if boxes_np and confidences_np:
index = cv2.dnn.NMSBoxes(boxes_np,confidences_np,0.25,0.45).flatten()
return boxes_np, confidences_np, index
