A method for detecting dead fish on large water surfaces based on improved YOLOv10

TL;DR

This paper introduces an improved YOLOv10-based model with FasterNet backbone and enhanced feature fusion for rapid, accurate detection of dead fish on large water surfaces, addressing limitations of traditional methods.

Contribution

It proposes a novel end-to-end detection model with specific architectural improvements tailored for aquatic environments, outperforming existing YOLO models in accuracy and efficiency.

Findings

Significant improvements in precision, recall, and average precision over baseline YOLOv10n.

Reduced model size and parameters while maintaining high inference speed.

Effective detection of small dead fish objects in large-scale aquaculture settings.

Abstract

Dead fish frequently appear on the water surface due to various factors. If not promptly detected and removed, these dead fish can cause significant issues such as water quality deterioration, ecosystem damage, and disease transmission. Consequently, it is imperative to develop rapid and effective detection methods to mitigate these challenges. Conventional methods for detecting dead fish are often constrained by manpower and time limitations, struggling to effectively manage the intricacies of aquatic environments. This paper proposes an end-to-end detection model built upon an enhanced YOLOv10 framework, designed specifically to swiftly and precisely detect deceased fish across extensive water surfaces.Key enhancements include: (1) Replacing YOLOv10's backbone network with FasterNet to reduce model complexity while maintaining high detection accuracy; (2) Improving feature fusion in the Neck section through enhanced connectivity methods and replacing the original C2f module with CSPStage modules; (3) Adding a compact target detection head to enhance the detection performance of smaller objects. Experimental results demonstrate significant improvements in P(precision), R(recall), and AP(average precision) compared to the baseline model YOLOv10n. Furthermore, our model outperforms other models in the YOLO series by significantly reducing model size and parameter count, while sustaining high inference speed and achieving optimal AP performance. The model facilitates rapid and accurate detection of dead fish in large-scale aquaculture systems. Finally, through ablation experiments, we systematically analyze and assess the contribution of each model component to the overall system performance.