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Research Topics in Object Detection using Deep Learning

Object Detection using Deep Learning

Research and Thesis Topics in Object Detection using Deep Learning

Object detection is a fundamental visual recognition problem in computer vision. Object detection is most prominently applied in surveillance, autonomous driving, pedestrian detection, animal detection, vehicle detection, face detection, text detection, pose detection, or number-plate recognition.

For the past decades, the quick advancement of deep learning technology has highly expedited the momentum of object detection, which is beneficial to attain significant improvement in object detection. Deep learning-based object detection-s significant benefits are stronger to occlusion, complex scenes, and challenging illumination.

The object detection setting of deep learning is categorized as bounding box and pixel-level localization. Present outstanding object detection paradigms using deep learning are divided into two-stage and one-stage detectors. The important feature representation learning strategies of deep learning-enabled object detection are multi-scale feature learning, contextual reasoning, and deformable feature learning.

Deep learning-based object detection has a diverse range of applications, such as sports production, robotics, productive analytics, security, transportation, medical, and military applications. The significant deep learning algorithms used to detect objects include convolutional neural networks, Fast R-CNN, and YOLO.

Promising Application of Object Detection Using Deep Learning

•  Object detection in the Retail industry - People counting systems, deep learning-based customer analysis, and queues detection are the cases of deep learning-based object detection in retail stores to understand customer-side information.
•  Autonomous Driving - Self-driving cars use object detection to identify pedestrians, traffic signs, other vehicles, and environmental obstacles, as object detection is the core concept in an automatic driving system.
•  Animal detection in Agriculture - In the area of agriculture, object detection is used for counting, animal monitoring, and estimation of the quality of agricultural products.
•  People detection in Security - A broad range of security applications in video surveillance and video analytics are based on object detection.
•  Object detection in Healthcare - Object detection in the medical sector has numerous applications, such as disease detection and diagnosis using medical images, scans, and photographs.
•  Face detection - In face detection, the images are detected using deep learning-based object detection to tackle the complications of different occlusion and illumination variations.
•  Pedestrian detection - Deep learning models enabled object detection helps to recognize pedestrians from natural surroundings. Recently, real-time pedestrian detection and cascaded pedestrian detection have been investigated.
•  Anomaly detection - Anomaly detection plays a vital role in fraud detection, healthcare monitoring, and weather inspection using deep learning-based object detection.
•  License plate recognition - In vehicular and traffic violation tracking, license plate recognition is important to reduce fraudulence. Deep learning and object detection have recently been applied in license plate recognition to provide a more robust and reliable solution.
•  Traffic sign recognition - To ensure security, deep learning-based object detection is applied for real-time accurate traffic sign recognition with high consistency.
•  Computer-Aided Diagnosis (CAD) system - Deep learning and object detection are jointly used for medical image detection for many disease diagnoses in the CAD system.
•  Event detection - Deep learning-enabled object detection helps to analyze multi-domain data for event detection from numerous video sequences and images in many application areas.
•  Pattern detection - Deep learning models analyze 2D and 3D images for pattern detection by contending duplicate patterns and periodic structure detections.
•  Image caption generation - In computer vision and NLP technologies, image caption generation is a challenging task. Various advanced deep learning models are applied for image captioning by overcoming the above challenge.
•  Salient object detection - Deep learning methods predict the saliency aspect of images from multi-level features to detect salient objects more accurately.
•  Text detection - Deep learning-enabled object detection helps detect cluttered natural scenes from the text image or object.
•  2D 3D pose detection - 2D and 3D human pose recognition and detection are conducted using deep learning architectures-based object detection.
•  Edge detection - Deep learning techniques are applied for edge detection by resolving the issues on the edges of different scales.

Most Significant Object Detection Algorithms

•  YOLO (You Only Look Once) - YOLO is the well-known, precise, and fast real-time object detection algorithm used in many applicative tasks under computer vision. Many versions and variants of YOLO have been developed to improve the performance and efficiency of object detection.
•  SSD (Single-shot detector) - SSD is a popular one-stage object detector that assists in predicting multiple classes to handle objects with different resolutions and sizes with better accuracy.
•  R-CNN (Region-based Convolutional Neural Networks) - R-CNNs are popular approaches that exploit deep learning models to object detection by selecting several proposed regions from images and labeling their classes and bounding boxes.
•  Mask R-CNN - Mask R-CNN is the improvement of Fast R-CNN for predicting an object mask.
•  SqueezeDet - SqueezeDet is a single-shot detector network specifically advanced for autonomous driving.
•  MobileNet - Mobile Net is a single-shot multi-box detection algorithm for object detection tasks.
•  YOLOR - YOLOR accomplishes highly enhanced object detection performance outcomes.