This chapter concludes the transformation of object detection and localizationLocalization, moving from traditional, handcrafted feature models to deep learningDeep learning systems. A significant driving force behind this change is convolutional neural networksConvolutional Neural Networks (CNNs) via hierarchical feature extractionFeature extraction, have helped increase accuracy and robustness when compared to previous models. Object detection literature has shifted toward two-stage-detection models, which emphasize accuracy but rely on operations before real-time detection is available, and single-stage models like the YOLO family of detectors, which offer real-time performance—necessary for deployments such as autonomous vehicles and intelligent surveillance applications. This chapter reviews the major areas of object detection applications from autonomous vehicles to medical imagingMedical imaging to intelligent surveillance to demonstrate the far-reaching impact and importance of this detection technology on society. The chapter also covers a handful of important training strategiesTraining strategies for training object detection methods that are relevant for developing deep learningDeep learning models that are both robust and accurate. Some training strategiesTraining strategies include data augmentationData augmentation, use of transfer learningTransfer learning, and a class of specialized loss functionsLoss functions to improve learned feature robustness. While there has been great progress in the object detection field, there are still problems specific to object prefixes that remain, including the ability to detect small, nearby objects, occlusionOcclusion detection, tradeoffs of computational overhead for edge devicesEdge devices, and dataset bias. Lastly, this chapter identifies numerous achievable research potential areas such as transformerTransformers-based modeling architectures, multimodal sensor fusionSensor fusion, 3D scene awareness, federated learningFederated learning, and explainable AIExplainable AI. With these efforts, we hope to experience more accurate, far-reaching, and ethically leveraged object detection methods, highlighting appealing properties pertinent to the bridge between AI and real-world performance experience.