This chapter discusses the important image processing andPreprocessing preprocessingImage preprocessing stages that are essential stages in developing a strong object detection system. It starts with a discussion of digital image representation, looking at binary images, grayscale and color images in the RGBRGB color space and HSVHSV color space models, and then discusses common file formats (JPEGJPEG format, PNGPNG format, TIFFTIFF Format) including their impact on processing efficiency and accuracy in models. The chapter will then discuss data annotationData annotation, which plays an important role, including techniques using bounding boxes and polygons, to keypoint annotation and instance segmentationInstance segmentation, and discusses tools including LabelImgLabelImg and CVATCVAT that can be used to create high-quality labeled datasets. A major concern of this chapter shall be data augmentationData augmentation techniques, which will be broadly organized into geometric transformations, color space transformations, and advanced techniques, such as CutMixCutMix and GANsGenerative Adversarial Networks (GANs) that may be used to artificially expand a training dataset, thus improving generalization and mitigating overfittingOverfitting. Finally, this chapter will wrap up with important preprocessingPreprocessing steps such as image normalizationNormalization, resizing, denoisingDenoising, and balancing annotations which are important for efficiently preparing a dataset for consumption by a model. This chapter discusses the challenges associated with unbalanced datasets and sets forth methods such as SMOTESMOTE and focal lossFocal loss, as well as the exciting opportunity of synthetic dataSynthetic data generation as a new and effective method of dealing with unbalanced datasets, as well as issues of scarcity and privacy. In general, the chapter provides practitioners with the methods to develop effective data pipelines, resulting in effective input data that is trained to be reliable and accurate in object detection.