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In recent decades, rapid advancements in machine learning have significantly enhanced the ability to detect and classify objects within digital images. These technological developments have facilitated a wide range of applications, including the identification of malignant cells in histopathological images, the classification of flora and fauna in ecological studies, the recognition of astronomical bodies, and the differentiation between authentic and synthetically generated (deepfake) images. In certain domains, automated systems have demonstrated performance that surpasses that of human experts. Despite these promising outcomes, several critical challenges must be addressed before such systems can be reliably and widely adopted. Key issues include the need for improved accuracy and robustness, the development of interpretable and transparent models, and the cultivation of user trust and societal acceptance. This Special Issue, "Object Detection and Image Classification", addresses some existing knowledge gaps. It consists of eight peer-reviewed papers that cover a range of new object detection algorithms and applications.