Portfolios
You Only Look Once
YOLO (You Only Look Once) is a state-of-the-art real-time object detection algorithm known for its speed and accuracy. Unlike traditional methods that scan an image multiple times, YOLO processes the entire image in a single pass using a deep neural network, making it highly efficient for real-time applications. It is widely used in fields like self-driving cars, security surveillance, and robotics due to its ability to detect multiple objects simultaneously with high precision.
In this project, we integrate YOLO with OpenCV to perform object detection on a live video feed. The model identifies and classifies various objects in real time, drawing bounding boxes around detected objects and labeling them with confidence scores. By leveraging OpenCV's deep learning module (cv2.dnn), we ensure optimized performance and seamless integration. This implementation demonstrates how AI-powered vision systems can be used in practical applications such as automated monitoring, AI-driven security, and autonomous navigation.
by:
Shahbaz Falahian
AI & Robotics Engineer
Microsoft AI Certified
OpenCV by YOLO
YOLO Object Detection in OpenCV
🔹 Category: Computer Vision | AI | OpenCV
🔹 Technology Used: OpenCV, YOLO, Python
Introduction
Briefly explain the concept:
"YOLO (You Only Look Once) is a real-time object detection algorithm used for recognizing multiple objects in images and videos with high accuracy. In this project, we implement YOLO using OpenCV to detect objects in a live video feed."
Step-by-Step Implementation
Load YOLO Models , Filter Warnings
Perform Object Detection
Result
Chatbots
We’ll create a chatbot that understands user input and responds intelligently. This will be a Flask-based chatbot with NLP using Python or OpenAI.
ChatBots
Why Chatbots ?
In today's digital age, businesses must provide fast, efficient, and engaging customer service to stay ahead of the competition. Chatbots have emerged as a game-changing solution, offering 24/7 customer support, automating routine tasks, and improving user experiences. Whether for e-commerce, customer support, or internal business operations, chatbots are becoming a crucial tool for companies looking to scale and optimize their services.
Why Are Chatbots Important for Businesses?
24/7 Customer Support
Unlike human agents, chatbots work around the clock. Businesses can provide immediate responses to customer inquiries, reducing wait times and improving customer satisfaction.
Cost-Effective Solution
Hiring and training human support agents is expensive. Chatbots help businesses reduce labor costs while maintaining high-quality customer interactions.
Lead Generation & Sales
Chatbots can guide potential customers through the sales funnel by answering product-related questions, offering personalized recommendations, and even processing orders.
Enhanced User Experience
With AI-driven capabilities, chatbots can provide personalized responses based on user behavior, improving engagement and customer retention.
Data Collection & Analysis
Businesses can gather valuable customer insights through chatbot interactions, allowing them to improve services and make data-driven decisions.
Scalability
As businesses grow, handling large volumes of customer inquiries becomes challenging. Chatbots enable companies to scale customer service efficiently without increasing operational costs.
How British AI Agency Can Help Businesses Implement Chatbots
At British AI Agency, we specialize in designing and deploying AI-powered chatbot solutions tailored to your business needs. Our chatbot development services include:
Custom Chatbot Development
We create intelligent chatbots that align with your brand’s identity and serve specific business purposes—whether for sales, customer support, or internal automation.
Integration with Business Systems
Our chatbots seamlessly integrate with CRM systems, e-commerce platforms, and social media channels, ensuring smooth operations and better customer engagement.
AI-Powered Chatbots
Leveraging advanced AI models, we build smart chatbots capable of understanding natural language, learning from interactions, and providing accurate responses.
Website & Mobile Chatbot Implementation
We deploy chatbots on websites, mobile apps, and messaging platforms like WhatsApp, Facebook Messenger, and Telegram to reach customers wherever they are.
Automation & Workflow Optimization
From handling FAQs to booking appointments and processing orders, our chatbots automate repetitive tasks, allowing businesses to focus on core operations.
Ongoing Maintenance & Optimization
We provide continuous support, updates, and improvements to ensure your chatbot remains effective and aligned with your business goals.
British AI Agency is here to help businesses harness the power of AI-driven chatbots. Contact us today to discuss how we can build a customized chatbot solution that fits your needs and transforms your customer interactions.
by:
Charlotte Morphy
AI Eng
MicroSoft Certified
Safety by Computer Vision
AI-Powered Airport Security : Identifying Suspicious
Imagine an airport security team searching for a suspicious individual wearing black clothes in a crowded terminal. Traditional surveillance methods are slow and inefficient, but AI-powered security systems can instantly detect and track individuals based on clothing color, improving safety and response time.
At British AI Agency, we develop cutting-edge AI surveillance solutions using computer vision, OpenCV, and YOLO-based deep learning. Our system scans real-time airport security footage to automatically detect people wearing black shirts, helping authorities monitor potential threats, track individuals of interest, and enhance public safety.
Our AI-driven video analysis technology enables faster threat detection, real-time tracking, and automated security monitoring, reducing human error and strengthening airport security operations. By integrating machine learning, object detection, and smart surveillance, we ensure a safer, more efficient travel experience for passengers and staff.
Enhance airport security with AI-powered monitoring—contact British AI Agency to learn more about our advanced AI solutions for real-time threat detection and surveillance.
Shahbaz Falahian
AI & ML Engineer
Result:
As demonstrated in the video above, the program effectively detects and highlights only individuals wearing black clothing, while ignoring those dressed in other colors. This precision ensures that the system focuses solely on the specified criteria, enhancing security monitoring and automated surveillance.
Building on this capability, we can further refine the detection process by incorporating additional computer vision algorithms to identify specific objects associated with individuals. For example, we can extend the model to detect a person in black clothing carrying a gray suitcase, enabling more advanced tracking and real-time identification of potential security risks.
By integrating deep learning and object recognition techniques, this AI-powered system can be customized for various smart surveillance applications, such as airport security, law enforcement, and automated threat detection. This level of precision and automation enhances situational awareness, reduces false alerts, and ensures a faster response to critical incidents.
With continued advancements in AI-driven video analysis, security teams can benefit from intelligent monitoring solutions that deliver real-time, accurate, and efficient threat detection.
seo
SEO by AI
In today's digital landscape, search engine optimization (SEO) is no longer just about keywords—it's about leveraging artificial intelligence (AI) to enhance search rankings, user experience, and website performance. At British AI Agency, we integrate cutting-edge AI technologies to automate, analyze, and optimize websites for maximum visibility.
Ashley J
Web Design & SEO
How AI Enhances SEO Performance
1. AI-Powered Keyword Research & Strategy
Traditional keyword research is time-consuming and limited. Our AI-driven tools analyze search trends, competitor data, and user intent to identify the most profitable keywords for your industry. This ensures higher rankings and targeted traffic.
2. Automated Content Optimization
We use AI-powered natural language processing (NLP) to:
Improve content readability and structure for SEO ranking.
Generate SEO-friendly meta descriptions, headers, and alt text.
Optimise for Google’s RankBrain & BERT algorithms.
3. AI-Based Technical SEO Audits
Our advanced AI tools scan websites for technical issues affecting SEO, such as:
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Broken links & crawl errors
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Mobile-friendliness & Core Web Vitals
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Site speed optimization
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Schema markup implementation
4. Intelligent Backlink Analysis & Competitor Insights
AI helps track high-quality backlinks while analyzing competitor strategies to improve domain authority and search rankings.
5. AI-Powered Voice Search & Local SEO
We optimize websites for voice search compatibility, ensuring better rankings in Google Assistant, Siri, and Alexaresults. Additionally, AI-driven local SEO strategies enhance Google My Business (GMB) listings and map rankings.
Results You Can Expect
✔ Higher search rankings & organic traffic growth
✔ Improved user experience & lower bounce rates
✔ AI-powered insights for ongoing optimization
✔ Faster indexing & better visibility across search engines
🚀 Boost your website’s SEO with AI-driven strategies. Contact British AI Agency today to transform your digital presence!
How NLP Enhances SEO Performance
1. AI-Powered Keyword Optimization
🔹 Traditional keyword stuffing is outdated—Google’s BERT & RankBrain algorithms now focus on user intentrather than just matching keywords.
🔹 NLP tools analyze semantic relevance and suggest keywords that align with natural search queries.
🔹 We optimize for LSI (Latent Semantic Indexing) keywords, ensuring a website ranks for multiple variations of a search term.
2. Content Structuring & Topic Clustering
🔹 NLP helps in organizing content into topic clusters, improving website authority and ranking.
🔹 AI-driven content structuring ensures logical flow, better readability, and optimized header tags (H1, H2, H3).
🔹 It enhances semantic relationships between articles, increasing internal linking opportunities.
3. AI-Based Sentiment Analysis for Better Engagement
🔹 NLP-powered sentiment analysis evaluates user behavior and feedback to refine content tone and engagement strategies.
🔹 AI identifies positive and negative sentiments in user interactions, helping in content personalization and higher conversion rates.
4. Voice Search Optimization with NLP
🔹 Over 50% of searches are voice-based, making NLP essential for conversational queries.
🔹 We optimize content for long-tail, natural-sounding phrases that align with how people speak.
🔹 NLP helps structure content for featured snippets, increasing visibility in voice search results.
5. AI-Driven Content Generation & Optimization
🔹 NLP-powered tools like ChatGPT, GPT-4, and Google’s MUM help generate SEO-friendly, high-quality content.
🔹 AI identifies gaps in existing content, recommending updates based on trending topics and competitor analysis.
🔹 NLP ensures content coherence, readability, and relevancy—essential for higher engagement and dwell time.
Results You Can Expect
✔ Higher organic rankings with semantic search optimization
✔ More featured snippets & voice search visibility
✔ Better engagement through AI-driven sentiment analysis
✔ Data-driven content strategies for ongoing SEO growth
🚀 Leverage NLP for smarter SEO strategies—partner with British AI Agency for AI-powered content and search optimization!
Lane Detection
and
Lane Departure
Introduction to Lane Detection and Lane Departure Warning System
Autonomous driving and advanced driver assistance systems (ADAS) have become essential in modern automotive technology. One of the critical components of these systems is lane detection, which enables vehicles to recognize and track road lanes in real time. Accurate lane detection plays a vital role in ensuring safe navigation, preventing unintended lane departures, and enhancing overall driving safety.
This project focuses on developing a Lane Detection and Lane Departure Warning System using OpenCV. The system detects lane markings on a road, identifies their boundaries, and provides alerts if a vehicle deviates from its lane. The implementation involves a series of computer vision techniques, including edge detection, region-of-interest (ROI) extraction, Hough Transform for line detection, and lane tracking algorithms.
The primary objectives of this project are:
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Detecting lane markings in real-time using image processing techniques.
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Tracking lane boundaries and differentiating between left and right lanes.
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Identifying lane departure by monitoring vehicle position relative to lane boundaries.
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Enhancing driving safety by providing warnings when unintended lane departure occurs.
This system serves as a foundation for autonomous vehicle research, driver assistance applications, and real-time road safety monitoring. It demonstrates the integration of computer vision, OpenCV, and AI-driven decision-making to improve driving efficiency and road safety.
Below is the Python code for Lane Detection and Lane Departure Warning System using OpenCV. It follows the structured approach we discussed:
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Read the video
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Convert to grayscale
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Apply Gaussian blur to reduce noise
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Detect edges using Canny Edge Detection
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Define the region of interest (ROI)
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Apply Hough Line Transform to detect lane lines
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Overlay detected lanes on the original video
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Detect lane departure by checking vehicle position
Shahbaz Falahian
AI / ML Engineer
import cv2
import numpy as np
def region_of_interest(image):
height, width = image.shape[:2]
mask = np.zeros_like(image)
# Define a polygon to focus on road lanes
polygon = np.array([[
(int(0.1 * width), height), # Bottom-left
(int(0.9 * width), height), # Bottom-right
(int(0.6 * width), int(0.6 * height)), # Top-right
(int(0.4 * width), int(0.6 * height)) # Top-left
]], np.int32)
cv2.fillPoly(mask, polygon, 255)
masked_image = cv2.bitwise_and(image, mask)
return masked_image
def detect_lanes(frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
edges = cv2.Canny(blur, 50, 150) # Edge detection
roi = region_of_interest(edges)
# Hough Line Transform for detecting lane lines
lines = cv2.HoughLinesP(roi, 1, np.pi/180, threshold=50, minLineLength=100, maxLineGap=50)
return lines
def draw_lanes(frame, lines):
if lines is not None:
for line in lines:
x1, y1, x2, y2 = line[0]
cv2.line(frame, (x1, y1), (x2, y2), (0, 255, 0), 5) # Draw lane lines in green
return frame
def lane_departure_warning(frame, lines):
height, width = frame.shape[:2]
center_x = width // 2 # Vehicle's assumed position
left_lane_x = None
right_lane_x = None
if lines is not None:
for line in lines:
x1, _, x2, _ = line[0]
x_avg = (x1 + x2) // 2
if x_avg < center_x:
left_lane_x = x_avg
else:
right_lane_x = x_avg
if left_lane_x and right_lane_x:
lane_center = (left_lane_x + right_lane_x) // 2
if abs(lane_center - center_x) > 50: # Threshold for lane departure
cv2.putText(frame, "LANE DEPARTURE WARNING!", (50, 50),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
return frame
# Video processing loop
cap = cv2.VideoCapture("Your Video Address.mp4") # Replace with actual video file
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
lines = detect_lanes(frame)
frame = draw_lanes(frame, lines)
frame = lane_departure_warning(frame, lines)
cv2.imshow("Lane Detection & Departure Warning", frame)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
OUTPUT:
How This Code Works
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Reads a video feed and processes each frame.
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Detects edges using Canny Edge Detection.
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Applies a mask to focus on the road (Region of Interest).
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Detects lane lines using the Hough Transform.
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Draws lane lines on the video feed.
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Calculates lane deviation and triggers a lane departure warning if the vehicle moves out of its lane.