Image degradation caused by environmental factors such as suspended solids and plankton, such as low contrast, blur, and color distortion, remains a major issue in underwater object detection, and research has been conducted in recent years to improve accuracy through deep learning and underwater image enhancement. However, approaches to quantitatively verify the effect of image degradation caused by underwater environmental factors on the accuracy of object detection have not been sufficiently investigated. In this paper, we analyzed the change in object detection accuracy by reproducing image degradation caused by underwater environmental factors at various scales. The relationship between image degradation and object detection accuracy was quantitatively evaluated by regression analysis, and it was clarified that the Sharpness Decrease Rate and RGB Entropy Decrease Rate significantly affect object detection performance.