Small Object Detection is an emerging field in computer vision, focusing on identifying and classifying objects of relatively small size within images. This is particularly challenging due to limited pixel information and the difficulty in distinguishing these objects from their surroundings. The aim of this thesis is to explore and develop innovative methods for effectively detecting small objects, with potential applications in areas such as surveillance, medical imaging, and autonomous vehicles.
The primary objectives of this project include:
- Investigating current algorithms and techniques in small object detection.
- Developing a novel approach/model to improve detection accuracy.
- Evaluating the performance of the proposed solution against existing benchmarks.
he MS COCO dataset is a large-scale object detection, image segmentation, and captioning dataset published by Microsoft. Machine Learning and Computer Vision engineers popularly use the COCO dataset for various computer vision projects. Understanding visual scenes is a primary goal of computer vision; it involves recognizing what objects are present, localizing the objects in 2D and 3D, determining the object’s attributes, and characterizing the relationship between objects. Therefore, algorithms for object detection and object classification can be trained using the dataset.
This dataset contains aerial imagery from UAVs to facilitate small object detection using deep learning. This version of the dataset contains a training, validation, and test split. Training: 717 samples Validation: 84 samples Test: 43 samples
Drones, or general UAVs, equipped with cameras have been fast deployed to a wide range of applications, including agricultural, aerial photography, fast delivery, and surveillance. Consequently, automatic understanding of visual data collected from these platforms become highly demanding, which brings computer vision to drones more and more closely. We are excited to present a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, named VisDrone, to make vision meet drones. The VisDrone2019 dataset is collected by the AISKYEYE team at Lab of Machine Learning and Data Mining , Tianjin University, China. The benchmark dataset consists of 288 video clips formed by 261,908 frames and 10,209 static images, captured by various drone-mounted cameras, covering a wide range of aspects including location (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and country), objects (pedestrian, vehicles, bicycles, etc.), and density (sparse and crowded scenes). Note that, the dataset was collected using various drone platforms (i.e., drones with different models), in different scenarios, and under various weather and lighting conditions. These frames are manually annotated with more than 2.6 million bounding boxes of targets of frequent interests, such as pedestrians, cars, bicycles, and tricycles. Some important attributes including scene visibility, object class and occlusion, are also provided for better data utilization.
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