Human-induced marine pollution has led to increasing underwater debris, threatening ecosystems. Although vision-based robots like AUVs and ROVs assist in seafloor monitoring, effective detection remains difficult due to poor visibility and degraded image quality in underwater environments. In this paper, we propose TrashTracer, a novel and lightweight framework for real-time underwater debris detection. To address visibility degradation, we introduce CG-Enhance, an online image enhancement method combining CLAHE and Gamma correction, which improves image quality without adding inference overhead. We further design an Efficient Reparameterized Backbone (ERB) utilizing the Diverse Branch Block (DBB) to enhance feature learning during training and simplify computation during inference. Additionally, we propose the Reparameterized Adaptive Spatial Fusion Head (RASF-Head) to adaptively integrate multi-scale features for robust detection. Extensive experiments on the TrashCan dataset demonstrate that our model achieves superior detection accuracy while maintaining low computational complexity and real-time performance, highlighting its strong potential for practical deployment in underwater debris monitoring applications.