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This survey paper specially analyzed computer vision-based object detectionchallenges and solutions by different techniques. We mainly highlighted objectdetection by three different trending strategies, i.e., 1) domain adaptive deeplearning-based approaches (discrepancy-based, Adversarial-based,Reconstruction-based, Hybrid). We examined general as well as tiny objectdetection-related challenges and offered solutions by historical andcomparative analysis. In part 2) we mainly focused on tiny object detectiontechniques (multi-scale feature learning, Data augmentation, Training strategy(TS), Context-based detection, GAN-based detection). In part 3), To obtainknowledge-able findings, we discussed different object detection methods, i.e.,convolutions and convolutional neural networks (CNN), pooling operations withtrending types. Furthermore, we explained results with the help of some objectdetection 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 performedcomparative analysis on different datasets such as MS-COCO, PASCAL VOC07,12,and ImageNet to analyze results and present findings. At the end, we showedfuture directions with existing challenges of the field. In the future, ODmethods and models can be analyzed for real-time object detection, trackingstrategies.

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