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A09 ITAI 1378 CV Cheat Sheet (DRAFT) by

The primary goal of this cheat sheet is to enable students to consolidate and articulate key concepts, methodologies, and tools pertinent to object detection in a succinct and accessible manner. This will be achieved through the creation of a basic cheat sheet which will serve as a quick reference guide for object detection tasks.

This is a draft cheat sheet. It is a work in progress and is not finished yet.

Basics of Object Detection

Object Detection. A computer vision technique used to identify and locate objects in an image or video with bounding boxes.
Annota­tions. Labeled data (images and their corres­ponding bounding boxes) used to train object detection models.
Inters­ection over Union (IoU). Measures the overlap between the predicted bounding box and the ground­-truth bounding box.
Bounding Boxes. Rectangles drawn around detected objects, repres­ented as (x_min, y_min, x_max, y_max).
Confidence Scores. Probab­ility score repres­enting the model’s confidence in detecting an object.
IoU = Area of Overlap / Area of Union Higher IoU indicates better accuracy in object locali­zation.

Challenges in Object Detection

Objects may vary in size, position, and rotation.
Part of the object might be hidden or blocked by another object.
Some classes may have fewer examples than others, affecting model perfor­mance.
Trade-off between detection accuracy and speed for real-time applic­ations.
Model performs well on training data but poorly on unseen data.
 

Common Object Detection Algorithms

R-CNN (Regio­n-based Convol­utional Neural Network). Uses Selective Search to generate region proposals. Extracts features using CNN and applies classi­fiers to each region.
Fast R-CNN. Improves R-CNN by using a shared CNN feature map for all region proposals. Introduces the RoI (Region of Interest) pooling layer.
Faster R-CNN. Replaces Selective Search with a Region Proposal Network (RPN). Achieves faster region proposal genera­tion.
SSD (Single Shot MultiBox Detector). Detects objects in a single forward pass. Uses feature maps from multiple layers for detecting objects of various sizes.
YOLO (You Only Look Once). Treats object detection as a single regression problem. Divides the image into a grid and predicts bounding boxes and class probab­ilities directly. Known for speed and real-time perfor­mance.

Tools and Libraries Overview (1/2)

Tensorflow
Keras
Instal­lation: pip install tensorflow
Instal­lation: pip install keras
Widely used for creating custom object detection models.
Provides high-level APIs to build and train models.
 
Example: model.f­it­(x_­train, y_train, epochs=10)
 

Object Detection Workflow

Step 1: Data Collection and Annota­tion.
Step 2: Data Prepro­cessing (resizing, normal­iza­tion).
Step 3: Model Selection (R-CNN, SSD, YOLO, etc.).
Step 4: Model Training (using frameworks like TensorFlow or PyTorch).
Step 5: Model Evaluation (using metrics like IoU, precision, recall).
Step 6: Model Optimi­zation (hyper­par­ameter tuning, model pruning).
Step 7: Deployment (integrate the model into applic­ati­ons).

Tools and Libraries Overview (2/2)

OpenCV
PyTorch
Instal­lation: pip install opencv­-python
Instal­lation: pip install torch torchv­ision
Useful for image processing tasks like resizing and augmen­tat­ions.
Known for dynamic comput­ational graphs, making it flexible for research.
Example: cv2.im­rea­d('­ima­ge.j­pg')
Example: torch.n­n.M­odule for creating custom models.