This chapter discusses the most important issuesLocalization facing modern object detection and localizationLocalization systems. It begins with object appearance variability, due to changes in lighting, occlusionOcclusion, viewpoints, and intra-class variation, because reliable feature extractionFeature extraction is impossible under frequently encountered variance. The chapter likewise discusses the issue of scale variation, including how difficult it is to detect objects of different size, as well as the trade-offs obtained with image resolution. Speed-accuracy trade-off for real-time applications and computational bottleneck with edge deployment is further discussed. Finally, these discussions include class imbalance and background clutter, which result in bias in model training and false detections. While expounding these key issues, the chapter synthesizes these issues to give a comprehensive introduction to the research of better robust and more generalizable detection systems.