The field of computer vision is witnessing significant advancements in object detection and image segmentation. Researchers are focusing on developing more efficient and accurate algorithms to address challenges such as size-invariant detection, sex-based bias in evaluation metrics, and detection in complex environments. Notably, innovative approaches are being proposed to mitigate the impact of size imbalance in object detection and to optimize image segmentation techniques. These developments have the potential to enhance the performance of various applications, including image processing, medical image analysis, and autonomous systems. Some noteworthy papers in this area include: The paper Towards Size-invariant Salient Object Detection proposes a generic evaluation and optimization framework to address the size-invariant property in salient object detection. The paper Fast OTSU Thresholding Using Bisection Method presents an optimized implementation of the Otsu thresholding algorithm using the bisection method, reducing computational complexity and preserving segmentation accuracy.