Abstract: Photovoltaic panel arrays can often shade over the fishing and light complementary ponds. BeiDou/GPS positioning signals are then affected to significantly reduce the autonomous navigation accuracy of unmanned workboats. Additionally, traditional machine vision can easily cause the suboptimal visual navigation of line detection, due to the low robustness. The resulting images are also confined to the variations in the light and shadow, the distribution of aquatic plants, and surface obstacles. In this study, an improved YOLOv8n model was proposed to extract the navigation center line in the fish and light complementary ponds. Firstly, the HGNetV2 network was used as the backbone network, in order to improve the real-time detection. Batch normalization (BN) and shared convolution structure were also used to design a lightweight detection head network, in order to reduce the size of the model. Then the SPPF_LSKA module was used as the feature fusion layer to improve the multi-scale feature fusion of the model. Finally, the Wise-IoU (weighted interpolation of sequential evidence for intersection over union) loss function was used to improve the bounding box regression performance and the detection accuracy of remote small targets. The detection frame coordinates of improved YOLOv8n were used to extract the reference points for the positioning of the cement columns on both sides. The lines of the cement columns on both sides were fitted by the least square method. The middle line of the navigation was then extracted by the angle bisection line. Ablation test results showed that the calculation amount, parameter number and model volume of the improved YOLOv8n model decreased by 36.0%, 36.8% and 32.8%, respectively, compared with the original, where the mean average precision (mAP) was 97.9%. The detection speed increased by 42.9%, where the accuracy was 93.1%, and the detection time of a single image was 6.8 ms. Comparison test showed that the improved YOLOv8n model exhibited the smallest size and the highest degree of lightweight, while maintaining a high level of detection accuracy, compared with the YOLOv5s, YOLOv6, YOLOv7, and YOLOv8. Excellent performance was also achieved in detecting the concrete columns. In the positioning test of the navigation center line, the average linear errors between the reference point of the extraction cement column and the manual observation mark were 3.69 and 4.57 cm in the range of 0-5 m and 5-10 m, respectively. The average linear error between the extracted and actual navigation center line was 3.26 cm, with an accuracy of 92%. The real-time test showed that the average extraction speed of the navigation midline was improved by 38.04% (117.01 frame/s) on the Windows11 test platform, compared with the original. Furthermore, the average extraction speed of navigation midline increased by 36.38% (22.34 frame/s) on the Jetson test platform. Consequently, the improved model can fully meet the navigation requirements of unmanned fishing and light complementary pond boat. The findings can provide a strong reference for the subsequent research on the visual navigation system of operation boats.