In recent years, deep learning-based object detection research has gained high attention because of its powerful learning capabilities and benefits in dealing with occlusion, scale alterations, and background transitions. Object detection is applied in various computer vision applications, including image retrieval, security, surveillance, automated vehicle systems, medical imaging, and machine inspection.
  Object detection attains great success in computer vision with the purpose of recognition and localization of one or more efficient targets from image or video data. Various techniques are applied for object detection, such as image processing, pattern recognition, artificial intelligence, and machine learning. Object detection also utilizes deep learning architectures owing to the tremendous advancements in deep learning technology in image classification. Deep learning-based object detection reaches prominence due to its broad spread of real-life applications. Multi-scale feature learning, contextual reasoning, and deformable feature learning are the significant feature representation learning techniques of deep learning-based object detection. Some deep learning architectures for object detection are convolutional neural networks, Fast R-CNN, and YOLO.
  Outstanding applications of deep learning-based object detection are Face Detection, Pedestrian Detection, Anomaly Detection, License Plate Recognition, Traffic Sign Recognition, Computer Aided Diagnosis (CAD) System, Event Detection, Pattern Detection, Image Caption Generation, Salient Object Detection, Text Detection, 2D 3D Pose Detection, Edge Detection, Point Cloud 3D Object Detection, and Fine-Grained Visual Recognition.
  Various studies show that object detection with deep learning needs further investigations by focusing on future research trends such as Video Object Detection, Weakly Supervised Object Detection, Multi-Domain Object Detection, Salient Object Detection, Multi-task Learning, Unsupervised Object Detection, Remote Sensing Real Time Detection, and GAN based Object Detectors.
  Several research surveys and reviews are conducted on deep learning-based object detection that describes detection components, learning strategies, future research areas, state-of-art approaches, applications, and benchmarks. Such popularly published literature surveys and reviews are listed below;