RLDD Detection Head: Efficient Shrimp Disease Detection

• RLDD Detection Head is a reparameterized design that fuses multi-branch convolutional training with inference-stage reparameterization to enhance feature extraction and reduce computational load.
• It reduces the parameter count by up to 32% while boosting precision by over 5%, ensuring effective detection in resource-scarce contexts.
• Its integration with multi-scale and attention modules enables rapid, real-time disease surveillance in shrimp aquaculture and similar applications.

The RLDD (Reparameterized Lightweight Disease Detection) detection head refers to a novel detection head architecture developed as part of a lightweight object detection model for shrimp disease identification. Designed to address the limitations of traditional decoupled detection heads in YOLOv8n—in particular their elevated parameter count and computational complexity—the RLDD detection head introduces a reparameterization scheme that balances accuracy with markedly improved efficiency. This design supports rapid and accurate detection, particularly within resource-constrained environments relevant to aquaculture and biological surveillance (2507.02354).

1. Architectural Structure of the RLDD Detection Head

The RLDD detection head departs from the standard decoupled head of YOLOv8n, which typically employs separate branches for classification and regression, each incorporating two 3×3 convolution layers and a 1×1 convolution. While decoupling reduces task interference, this setup increases both parameter count and computational demands.

The RLDD detection head employs a two-stage design:

• Multi-branch Training Structure: During training, feature maps at multiple levels (P3, P4, P5) are first unified in channel dimension via a 1×1 convolution. Each feature map is then processed through two reparameterized 3×3 convolutional layers ("RepConv"). Each RepConv comprises parallel branches: a 3×3 convolution, a 1×1 convolution, and a 3×3 average pooling operation. This multi-branch setup fosters the extraction of richly diverse and multi-scale features, which is particularly significant for detecting various disease patterns manifesting at different scales.
• Inference-stage Reparameterization: At inference time, all parallel branches are mathematically merged into a single 3×3 convolution operation. This fusion process preserves the expressive capability of the multi-branch training structure while ensuring inference is efficient and minimally redundant (“lossless acceleration”). As a result, the head's complexity is substantially reduced without sacrificing the quality of learned representations.

2. Computational Efficiency and Accuracy Trade-off

The introduction of reparameterization enables the RLDD head to optimize the accuracy-efficiency balance:

• Parameter Reduction: By folding the training-time multi-branch structure into a single convolutional operation, the number of parameters in the head is significantly lowered. Experiments indicate a decrease from 3.1M to 2.3M parameters (~25.8% reduction) when the RLDD head replaces the baseline YOLOv8n detection head.
• Precision Maintenance and Enhancement: Despite a reduced parameter footprint, the multi-branch strategy ensures that the feature richness needed for accurate disease detection is not compromised. In ablation studies, the RLDD head alone achieved a precision increase of 5.3% compared to the baseline.
• Impact on Inference Throughput: The conversion to a single 3×3 convolution for inference minimizes memory operations and computational costs, conferring benefits for real-time applications and deployment on edge devices.

3. Integration with C2f-EMCM and SegNext_Attention Modules

The RLDD detection head operates as part of an ensemble of architectural enhancements:

• C2f-EMCM (Efficient Multi-scale Convolution Module): Replaces the original fixed-size convolutional blocks in YOLOv8n’s C2f module. It splits the input feature map into two channel groups—one passes as "original features," and the other is processed through separate 3×3 and 5×5 convolutions, capturing multi-scale semantic information. The resultant tensors are concatenated and projected with a 1×1 convolution for improved cross-channel interaction and reduced redundancy.
• SegNext_Attention: This self-attention mechanism, based on the MSCA (Multi-Scale Convolutional Attention) module, further enhances the feature extraction capabilities by leveraging deep and depthwise separable convolutions for multi-scale spatial aggregation, combined with adaptive attention:

$$\text{Att} = \operatorname{Conv}_{1 \times 1} (\operatorname{Scale} (\operatorname{DW.Conv} (\operatorname{Conv}(f))))^3$$

$\text{Out} = \text{Att} \odot f$

Here, $f$ is the input feature map, $\operatorname{DW.Conv}$ denotes depthwise separable convolution, and $\odot$ is element-wise multiplication. This module allows the network to focus on salient regions and mitigate background noise—an especially relevant capability when disease markers are subtle or distributed.

4. Experimental Performance and Comparative Analysis

Extensive experiments demonstrate the practical value of the RLDD detection head:

• Main Validation Dataset: On the bespoke shrimp disease dataset, the model incorporating RLDD achieved a [email protected] of 92.7%, a 3% improvement over the baseline YOLOv8n implementation.
• Parameter and Resource Footprint: The integration of RLDD, EMCM, and SegNext_Attention reduced the parameter count by 32.3% to 2.1M, supporting the model's suitability for real-time and embedded scenarios.
• Ablation Insights: RLDD led to a 5.3% rise in precision and a substantial reduction in model size; EMCM contributed an 8.1% gain in precision in isolation. The full integration of all modules yielded the best overall balance between accuracy and efficiency.
• Generalization: On the URPC2020 dataset, RLDD-equipped models showed a 4.1% [email protected] advantage over YOLOv8n, demonstrating robust generalization across domains.

Table: Main Results of RLDD Integration

Module Configuration	Precision (%)	[email protected] (%)	Params (M)
YOLOv8n baseline	Baseline	Baseline	3.1
RLDD only	+5.3	Improved	2.3
RLDD + EMCM + SegNext_Attn	Best	92.7	2.1

5. Practical Implementation and Usage Contexts

The RLDD detection head is particularly suited for deployment scenarios that demand:

• Edge Deployment: The reduction in parameters and computational load allows the model to run efficiently on embedded or edge devices, relevant to field-based aquaculture monitoring.
• Disease Surveillance in Variable Scales: The combined multi-scale and attention-based design ensures robustness to target size variability—an essential property for detecting diverse disease manifestations in shrimp aquaculture.
• Transferability: Ablation and comparative studies suggest that the RLDD approach can serve as a general template for constructing lightweight detection heads in other resource-constrained detection tasks.

This suggests potential for further applications in IoT-enabled agriculture, mobile health diagnostics, and other real-time detection domains where model footprint and speed are critical constraints.

6. Summary and Implications

The RLDD detection head exemplifies an effective architectural innovation for lightweight object detection. Through the use of multi-branch training and inference-time reparameterization, it offers reduced parameter count and computational burden without sacrificing detection performance. Its integration with modules for multi-scale feature extraction and adaptive attention further enhances its applicability to complex and heterogeneous detection settings. The empirical results affirm that such design decisions can yield substantial improvements in both precision and efficiency, providing a compelling foundation for intelligent disease detection and similar vision-based analytic tasks in resource-sensitive contexts (2507.02354).