## 9 Conclusions and Future Scope  
Applications like machine vision and self-driving cars [276] consider object detection as the fundamental step. This paper comprehensively reviews both traditional object detectors and deep learning-based object detectors starting from RCNN and going all the way to the latest CornerNet, with its pros and cons. We have summarized different object detectors' performances on MS-COCO and Pascal-VOC datasets. Various deep learning frameworks and available API services for object detection are briefly summarized. Since all CV algorithms are trained and verified using benchmark datasets only, we covered widely used datasets for specific applications; datasets play a crucial role in achieving the correct output. Image classification dataset challenges' statistics, and also PASCAL-VOC and ILSRVC comparisons, which are released by worldwide competitions, are also presented. Deep learning-based object detection technology is developing rapidly day to day due to upgrading computational power. In order to deploy deep learning-based object detectors in applications like self-driving cars, high-precision, real-time systems are urgently needed. The ultimate goal while deploying deep learning-based object detectors to real-time scenarios is to achieve both higher accuracy and speed. Researchers are moving in this direction and have improved processing speed, constructed new architectures and lightweight feature extractors, solved complex environment scenes like small and occluded objects, improved localization accuracy, improved confidence scores, enhanced post-processing methods, made anchor-less detectors to overcome the data imbalance problem during training, done object detection with the latest engines, detected with better features, i.e., Feature Fusion, designed new loss functions, enhanced object detection by semantic segmentation, performed scale-adaptive detection, trained from scratch, and achieved better results by combining both one-stage and two-stage detectors. With the day to day increase of powerful, deep learning-based object detectors, we covered their use in fields such as pedestrian detection, face detection, the military, medical image analysis, intelligent transportation systems, crowd detection, and sports, and still, applications in the object detection domain are arising. Although there is great success in this domain, there is much scope for further development. Comparisons to overcome challenges and difficulties raised using various deep learning-based object detection methods and speed up techniques are also covered. In this paper, various GPU-based embedded platforms and a quantitative performance comparison between Jetson variants and GPU-based offline work station for real-time object detection using deep learning techniques are also assessed. Real-time object detection using different GPU-based embedded platforms should be robust with respect to invariant occlusions, scales, illumination, intra-class variations, and deformations. Due to these factors, there are high chances of not detecting small objects in a video, which reduces the performance of real-time object detection systems. Object detection is a fundamental problem to be solved; existing methods were developed. But still, there is a huge scope for developing new mechanisms and object detection as basic services in real-time applications, such as deep-sea bases, driverless cars, robots navigating on planets, industrial plants, and drone cameras where high precision is expected for certain tasks. Particularly, for detecting some small objects there remains a large speed gap between human eyes and machine vision. The future of object detection would be AutoML; i.e., designing a detection model to reduce human intervention. For more accurate detection, much data is required. To improve overall accuracy further, training images with more diversity (scale, view-angle) of the object are needed.