Object Detection Techniques

Object detection has evolved significantly with each new model bringing transformative improvements in both speed and accuracy.

Theoretical Background

• R-CNN (Region-Based Convolutional Neural Networks): Proposed by Girshick et al., R-CNN combines region proposals with CNNs for object detection. It extracts around 2,000 region proposals using selective search and then classifies each with a CNN. This method is accurate but computationally expensive.

• Fast R-CNN: This addressed R-CNN's speed issue by sharing CNN computations across proposals. It uses a single CNN to extract a feature map and introduces an ROI pooling layer, significantly reducing the time needed for detection.

• Faster R-CNN: Introduced the Region Proposal Network (RPN) to generate region proposals, integrating them into the CNN framework, which speeds up the process and improves accuracy.

• YOLO (You Only Look Once): A breakthrough in speed, YOLO treats object detection as a regression problem, predicting bounding boxes and class probabilities in one evaluation. While early versions struggled with smaller objects, recent iterations have improved.

Practical Applications

These advancements have enabled real-time applications in autonomous driving, video surveillance, and robotics. Faster detection with YOLO has been particularly beneficial in settings where low latency is crucial.

Code Example

For a basic YOLO implementation using the darknet framework:

import cv2
import numpy as np
import time

# Load YOLO
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Load image
img = cv2.imread('image.jpg')
height, width, channels = img.shape

# Detecting objects
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

# Show information on the screen
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            # Object detected
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            # Draw bounding box, etc.

Further References

• R-CNN paper
• Fast R-CNN paper
• Faster R-CNN paper
• YOLO paper

Diagram

Below is a diagram illustrating the workflow evolution from R-CNN to YOLO:

graph LR
    A[R-CNN: Region Proposals + CNN] --> B[Fast R-CNN: ROI Pooling]
    B --> C[Faster R-CNN: RPN Integration]
    C --> D[YOLO: Single Network]

These advancements reflect a trend towards more integrated and efficient systems, enabling broader practical applications.