The chapter addresses changesLocalization brought about due to object detection and localizationLocalization with deep learningDeep learning. Models like YOLOYOLO, FasterR-CNN R-CNNFaster R-CNN, and SSDSingle shot multibox detector allow machines to find and classify objects in images and videos accurately. The discussion is divided into three primary applications of these systems. In autonomous drivingAutonomous driving, these systems are foregrounded because they allow machines to identify pedestrians, vehicles, and traffic signs, which is needed to guide vehicles through real-time navigation safely. In surveillance systemsSurveillance systems, object detection and localizationLocalization systems allow for automated threat detection, traffic observation, and retail observation, which ultimately enhance safety in private and public spaces. In medical imagingMedical imaging, object detection and localizationLocalization can help in diagnostic activities by identifying tumors, segmenting organs, and reviewing scans for computer aided diagnosis, thereby helping to lessen the time and improve the accuracy of diagnoses. While there are some challenges such as limitations on computational abilities and reservations about informing society about data privacy, work continues in these areas to stretch these boundaries of intelligent systems and advance towards a safer, more efficient, and automated engagement with our physical world.