Underwater trash detection faces many problems. For example, the resolution of underwater trash datasets is generally low, and trash may show irregular shapes, sizes, and scales, especially small objects that are difficult to detect. To solve the above problems, we propose UTD-YOLO, an improved YOLOv5s trash detection model. This model proposes a cross-layer aggregation spatial dimensionality reduction module (CASDRM) and channel-PAN-FPN with improved channel information. CASDRM preserves the information of the channel dimension and utilizes the receptive field between different layers to enhance the feature extraction ability while strengthening the ability to adapt to the geometric changes of objects. Channel-PAN-FPN realizes two-way information transmission and improves the recognition accuracy of the model. UTD-YOLO can achieve good recognition results for smaller-scale targets in low-resolution datasets. The experimental results show that the UTD-YOLO model improves the mAP50 to 73.2% on the Trashcan dataset, which is higher than the original YOLOv5 algorithm.