A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets

Deep learning (DL) and computer vision (CV) offered a trending role in object detection(OD), object tracking, pedestrian detection, and autonomous vehicles from the last few decades. Several old and recent approaches were proposed to solve these computer vision and deep learningbased problems with detection, tracking techniques, algorithms, and data sources. In recent decades, this field gained importance due to the rising interest in brain-inspired human recognition and detection technologies. Using online/offline data about images and videos, researchers are intensively modeling sentiments and computational analysis. Computer vision-based artificial neural networks (ANN) and convolutional neural network (CNN) based approaches providing robust solutions. This survey paper specially analyzed computer vision-based object detection challenges and solutions by different techniques. We mainly highlighted object detection by three different trending strategies, i.e., 1) domain adaptive deep learning-based approaches (discrepancy-based, Adversarial-based, Reconstruction-based, Hybrid). We examined general as well as tiny object detection-related challenges and offered solutions by historical and comparative analysis. In part 2) we mainly focused on tiny object detection techniques (multi-scale feature learning, Data augmentation, Training strategy (TS), Context-based detection, GAN-based detection). In part 3), To obtain knowledge-able findings, we discussed different object detection methods, i.e., convolutions and convolutional neural networks (CNN), pooling operations with trending types. Furthermore, we explained results with the help of some object detection algorithms, i.e., R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, which are generally considered the base bone of CV, CNN, and OD. We performed comparative analysis on different datasets such as MS-COCO, PASCAL VOC07,12, and ImageNet to analyze results and present findings. At the end, we showed future directions with existing challenges of the field. In the future, OD methods and models can be analyzed for real-time object detection, tracking strategies.