# A Lightweight Detection Method for Meretirix Based on an Improved YOLOv8 Algorithm
## 1 Introduction  
The clam is widely cultivated due to its rapid growth and high economic value; however, its complex and variable morphological characteristics pose challenges for efficient identification and classification in large-scale farming and automated processing [1, 2]. Clam detection models are commonly deployed on edge devices such as sorting machines and mobile terminals. They are widely used in scenarios like aquaculture farms and seafood processing lines, placing higher demands on detection accuracy and deployment performance. Therefore, it is of great significance to develop a target detection model that balances both high detection accuracy and lightweight design. In recent years, artificial intelligence technologies, particularly deep learning and convolutional neural networks (CNNs), have achieved remarkable progress in image recognition and object detection, driving their widespread adoption in the aquaculture industry. To improve detection accuracy, [PERSON] et al. [3] addressed the challenges associated with the small size, high transparency, and irregular morphology of Paralichthys olivacues larvae, achieving precise counting through the application of the ResNet34 model. [PERSON] et al. [4] enhanced feature extraction by employing an improved MobileNetv2 backboneintegrated with a coordinate attention module, resulting in a 3.2% increase in mean average precision (mAP) on a crab dataset. [PERSON] et al. [5] developed an improved YOLOv3 architecture (YOLO-D) by reinforcing the residual module, incorporating a dual attention mechanism, and constructing a secondary recursive feature pyramid network, thereby enhancing the detection accuracy of small objects in offshore UAV imagery. Similarly, [PERSON] et al. [6] optimized the DenseNet framework by integrating the SENet attention mechanism and refining the network structure, achieving a recognition accuracy of 91.53% on a custom dataset. Addressing the challenge of small target omission and false detection in aerial imagery, [PERSON] et al. [7] integrated Bi-PAN-FPN for multi-scale feature fusion into YOLOv8-s, utilized GhostblockV2 to reduce parameters and suppress information loss, and optimized anchor boxes through the WiseIoU method and a dynamic non-monotonic focusing mechanism, resulting in notable performance improvements on public datasets. Regarding model lightweighting, [PERSON] et al. [8] proposed an efficient object detection algorithm for external damage on power transmission lines, leveraging ACmix and ShuffleNetv2 to substantially reduce model parameters and computational load, while integrating the PC-ELAN module and SIOU loss function for enhanced performance. [PERSON] et al. [9] developed a lightweight SAR ship detector by combining a simplified U-Net architecture with an anchor-free detection framework, achieving model compression at the expense of some detection accuracy. Additionally, [PERSON] et al. [10] presented the YOLOv8-HD model, which employed optimized detection heads and deformable attention mechanisms to enhance feature extraction, achieving 77.6% mAP in stacked wheat seed scenarios while reducing model size by 20%. [PERSON] et al. [11] developed the lightweight YOLOv5-PRE model for apple detection, combining ShuffleNet and GhostNet to minimize model size and enhancing feature extraction with CA and CBAM attention mechanisms, ultimately achieving 94.03% accuracy with a 17.93% increase in detection speed. [PERSON] et al. [12] proposed YOLOv7-SDBB, a lightweight underwater object detection method that employs the ShuffleNetv2 backbone to reduce computational load, integrates D-ELAN and D-MPConv modules to accelerate detection, and utilizes BiFPN and BiFormer for improved feature fusion and accuracy. Validation on public datasets confirmed substantial reductions in model parameters and computation, while maintaining high accuracy, thereby achieving an effective balance between precision and efficiency. Currently, research on the detection and recognition of clam remains limited.