HiFi-Det: Collaborative Multi-Scale Frequency-Domain Feature Optimization for Crown-of-Thorns Starfish Detection in Complex Underwater Environments

Abstract

Outbreaks of the Crown-of-Thorns Starfish (COTS, Acanthaster spp.) are a leading biological driver of coral cover loss, making timely and accurate population monitoring essential for reef management. Conventional diver-based surveys are labor-intensive and prone to missed detections, motivating automated detection from underwater imagery. However, COTS detection in complex underwater scenes still faces three major challenges. First, COTS individuals are often very small and carry limited discriminative information, making them inherently difficult to detect. Second, low underwater contrast and complex coral textures blur target boundaries and cause targets to be easily confused with the background. Third, ecological monitoring values recall more highly than precision—missing a COTS individual is far more costly than a false alarm—yet the recall of existing detectors remains insufficient. To address these challenges, we propose HiFi-Det (High-resolution Frequency-integration Detector), a collaborative multi-scale frequency-domain feature optimization method built on YOLO11. HiFi-Det integrates three complementary enhancements: a high-resolution detection branch that strengthens feature representation for small targets; wavelet transform convolution (WTConv) modules in the backbone and neck that apply band-separated processing in the wavelet domain to improve discrimination of COTS targets from low-contrast, textured coral backgrounds; and a WIoUv3 bounding box regression loss that dynamically focuses on ordinary-quality samples to improve recall while maintaining precision. On the public Great Barrier Reef dataset, HiFi-Det attains 81.02% F2 and 87.54% mAP@50, surpassing the YOLO11 baseline by 3.00% and 2.57%, respectively, while keeping the parameter count essentially unchanged relative to the YOLO11s baseline (within 3%), so that the accuracy gains are obtained without inflating model size. Ablation studies confirm the synergy of the three components: the high-resolution branch preserves spatial details, WTConv suppresses background textures, and WIoUv3 further curbs false positives while sustaining high recall. Applying the same recipe to a larger YOLO11m backbone yields HiFi-Det-m, which likewise improves over that backbone in both F2 and recall, indicating that the approach is a transferable recipe rather than a single fixed architecture. These results show that task-specific architectural and training designs can effectively adapt generic detectors to the demands of underwater ecological monitoring.