Abstract: Accurate classification and detection have posed a great challenge on benthic organisms, due to the excessive parameters, computational overhead, and complex underwater environments. In this study, a lightweight target algorithm (named YOLOv7-RFPCW) was proposed to improve the detection accuracy of underwater benthic organisms. 1) The original YOLOv7 network architecture was reconstructed into the feature extraction, in order to significantly reduce the parameters and computational complexity. The overall footprint was effectively shrunk to simultaneously enhance the adaptability in the underwater scenes. This modification served as the lightweight solution. The deployment also remained computationally feasible in the resource-constrained underwater or real-time applications where processing speed was paramount. Progressive Efficient Lightweight Attention Network (P-ELAN) and its variant, P-ELAN-W, were integrated into the architecture to further lighten the network. The existing components were replaced or augmented to streamline the information flow, and then prune unnecessary computations for the essential feature representations. P-ELAN and P-ELAN-W contributed to a substantial reduction in the overall complexity. While the discriminatory power was preserved for the accurate detection of benthic organisms. A convolutional Block Attention Module (CBAM) was employed to recognize the color distortion and spatial localization in the underwater imagery. This attention mechanism was seamlessly integrated into the network to reinforce the feature fusion. The crucial visual cues were discerned in the context of the visually degraded underwater, in order to mitigate the information loss. The attention of CBAM networks was focused on the major features relevant to benthic organisms in the environmental factors of water turbidity and light scattering. 2) The default CIOU loss function was substituted with the Weighted Intersection over Union-Version 3 (WIOU-V3) loss function. The reason was that the shape deformation was commonly encountered with the underwater targets. WIOU-V3 was also tailored to handle the varying shapes and orientations of underwater benthic organisms, in order to provide the adaptive measure of bounding box prediction accuracy. The advanced loss function was incorporated to better cope with the underwater object shapes, thereby reducing the false negatives or positives from the shape-related misinterpretations. Extensive experiment was carried out to evaluate the efficacy of the YOLOv7-RFPCW. The results demonstrate that the parameters were significantly reduced by 75.9%, while there was a substantial decrease of 30.7% in the computational requirements. Moreover, the size of the model was compressed by 75.3%, indicating the profound impact of lightweight strategies. Notably, this efficiency was achieved in the high detection accuracy, which was improved by 1.9 percentage points, indicating successfully balanced compactness with high detection performance. In summary, the YOLOv7-RFPCW algorithm presented as the effective solution to detect underwater benthic organisms. The complexity and computational demands were fully met in the underwater environment, including color distortion, spatial localization errors, and target shape deformations. The improved model can be expected to serve as a robust and efficient tool in the practical deployment for the accurate detection of benthic organisms in underwater scenarios, where the model size and computational overhead were coupled with a tangible increase in detection precision. This finding can offer a reliable means to monitor these vital components of aquatic ecosystems with enhanced efficiency and reliability.