Atmospheric scattering caused by fog degrades image quality and significantly reduces the reliability of computer vision systems. Existing dehazing studies have mainly evaluated dehazing performance using pixel-level metrics such as PSNR and SSIM. However, these metrics do not fully reflect the actual impact of dehazing on downstream object detection performance. Therefore, this paper treats image dehazing as a preprocessing step for object detection in foggy environments and analyzes its effect using standard object detection evaluation metrics. The experimental results demonstrate that, under three fog-density conditions, $\beta =0.005$, $0.010$, and $0.020$, images processed by the DL-U-Net-based dehazing method achieved higher $\mathrm{mAP}@0.5$ values than the corresponding original hazy images, with relative improvements of $+0.39\%$, $+6.60\%$, and $+13.37\%$, respectively. Furthermore, under the dense fog condition of $\beta =0.020$, Recall improved more substantially than Precision. These results indicate that, as fog density increases, dehazing preprocessing becomes more effective in restoring object structural information, reducing missed detections, and enhancing downstream object detection performance.