Project Background:
Object detection revolves around the fundamental challenge of enabling machines to perceive and understand visual data as humans do. Object detection is a pivotal task within computer vision, aiming to identify and localize objects of interest within an image or video frame. This field has seen remarkable advancements largely driven by the surge in deep learning techniques and the availability of large-scale annotated datasets. Key objectives typically include improving detection accuracy, enhancing efficiency for real-time applications, and extending capabilities to handle diverse object categories and scenarios. Additionally, it involves addressing specific use cases such as autonomous driving, surveillance systems, and medical imaging. Object detection advancements contribute to technological innovation and hold significant potential for societal impact across various domains.
Problem Statement
- Enhance the accuracy of object detection algorithms to ensure reliable and precise identification and localization of objects in images or video frames.
- Improve the efficiency of object detection models to enable real-time processing, which is essential for applications like autonomous driving and surveillance systems.
- Ensure the object detection system can scale to handle large datasets and varying complexities of object categories and environmental conditions.
- Enhance the robustness of object detection models to handle challenges such as occlusions, varying scales, lighting conditions, and background clutter.
- Improve the interpretability to understand their decision-making process, which is critical for safety-critical applications and regulatory compliance.
- Develop methods to reduce the time and effort required for annotating training data crucial for training accurate object detection models.
- Investigate techniques for adapting object detection models to new domains with limited annotated data, ensuring their applicability in diverse real-world scenarios.
Aim and Objectives
- Develop advanced object detection algorithms for accurate and efficient real-time recognition of objects in images and videos.
- Enhance detection accuracy through innovative deep learning architectures.
- Optimize models for real-time performance without compromising accuracy.
- Explore methods for efficient annotation and data augmentation.
- Develop strategies for domain adaptation to diverse scenarios.
Contributions to Object Detection
- Novel deep learning architectures tailored for improved accuracy and efficiency.
- Techniques for enhancing robustness to occlusions, varying scales, and challenging conditions.
- Methods for efficient data annotation and domain adaptation.
- Tools and frameworks for deploying object detection models across diverse hardware platforms.
- Insights into interpretability and ethical considerations in object detection systems.
Deep Learning Algorithms for Object Detection
- YOLO (You Only Look Once)
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Faster R-CNN (Region-based Convolutional Neural Network)
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SSD (Single Shot Multibox Detector)
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RetinaNet
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Mask R-CNN
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R-FCN (Region-based Fully Convolutional Networks)
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FPN (Feature Pyramid Network)
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CenterNet
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EfficientDet
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Cascade R-CNN
Datasets for Object Detection
- COCO (Common Objects in Context)
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Pascal VOC (Visual Object Classes)
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ImageNet
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Open Images Dataset
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KITTI Vision Benchmark Suite
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MS COCO
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Cityscapes Dataset
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BDD100K (Berkeley DeepDrive 100K)
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SUN Database
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Caltech Pedestrian Detection Benchmark
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
