Project Title:Object Detection in Images using Deep Learning

Objective:
To build a model that can not only classify objects in an image but also locate them by drawing bounding boxes around each object.

???? Project Overview:

Object detection is a computer vision task that combines image classification and object localization. The goal is to identify what objects are present in an image and where they are. This project uses advanced deep learning models to detect multiple objects within images, enabling applications like autonomous driving, security surveillance, and retail automation.

???? Key Steps in the Project:

Data Collection:

Use labeled datasets such as:

COCO (Common Objects in Context)

Pascal VOC

Open Images Dataset

Each image comes with bounding box coordinates and class labels.

Data Preprocessing:

Resize and normalize images.

Convert annotation data into a usable format (e.g., XML to JSON).

Apply data augmentation while preserving box coordinates.

Model Selection:

Choose or build object detection models:

YOLO (You Only Look Once) – fast and accurate

SSD (Single Shot Detector) – efficient for real-time

Faster R-CNN – highly accurate, used for high-stakes tasks

Detectron2 – Facebook's modular object detection library

Model Training:

Use a pre-trained model (transfer learning) or train from scratch.

Fine-tune using backpropagation and annotated data.

Set appropriate IoU (Intersection over Union) thresholds for box accuracy.

Evaluation:

Metrics: mAP (mean Average Precision), IoU, Precision/Recall

Visual validation by plotting predicted bounding boxes on test images.

Deployment:

Create a web/mobile app where users can upload images for object detection.

Use frameworks like Flask, FastAPI, or Streamlit.

Export the model using ONNX, TensorFlow Lite, or OpenCV for real-time applications.

????️ Tools & Technologies:

Programming Language: Python

Libraries/Frameworks:

PyTorch, TensorFlow, OpenCV

YOLOv5/YOLOv8, Detectron2, MMDetection

LabelImg (for manual annotation), Streamlit (for UI)

✅ Applications:

Face and pedestrian detection

Vehicle detection for traffic management

Retail checkout systems

Wildlife monitoring

Industrial automation (defect detection)

???? Conclusion:

The Object Detection in Images project challenges students to apply deep learning to real-world visual tasks. It goes beyond simple classification by introducing localization and multi-object tracking, making it ideal for learning how advanced AI systems interpret visual scenes.