Microplastics in water pose significant environmental hazards. Current manual approaches for detecting and characterizing microplastics have inefficiency in time, cost, and labor. Recent advances in deep learning-based computer vision approaches have enhanced efficiency, while they suffer from low accuracy for various types of microplastics. This paper presents a size-adaptive ensemble deep learning approach, aiming to overcome the challenge of morphological diversity and automate the detection of microplastics. The approach employs two specialized YOLOv8 models, including a high-resolution model for small particles (e.g., pallets) and a standard-resolution model for large particles (e.g., fibers, films, fragments). The presented approach was implemented and experimentally evaluated using microscopy images. The results show that the approach improved detection accuracy (17.5% increase in mAP50, 32.1% increase in mAP50-95) compared with traditional unified deep learning models. This research advances the ability to automatically detect various microplastics based on computer vision and deep learning.