  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.
