With the increasing severity of global water pollution, underwater debris has become a serious threat to marine ecosystems. Autonomous underwater robots equipped with efficient debris detection systems can significantly increase underwater cleanup efforts. This paper proposes a high-precision and lightweight underwater debris detection method. To address the challenges posed by the diverse shapes and varying scales of underwater debris, we introduce an improved detection approach. Using YOLOv8 as the backbone, we replace conventional convolution with Alterable Kernel Convolution (AKConv), which dynamically adjusts the convolutional kernel shape on the basis of input features, enabling more accurate capture of object details. In the neck network, an improved high-level screening-feature fusion pyramid network (HSFPN) replaces the original YOLOv8 neck, incorporating coordinate attention instead of channel attention to enhance multiscale feature fusion. The experimental results demonstrate that the proposed AH-YOLO method achieves significant improvements in mAP50 and mAP50:95 on the TrashCan dataset compared with the original YOLOv8.