A lightweight ship target detection model based on improved YOLOv5s algorithm.

Real-time and accurate detection of ships plays a vital role in ensuring navigation safety and ship supervision. Aiming at the problems of large parameters, large computation quantity, poor real-time performance, and high requirements for memory and computing power of the current ship detection model, this paper proposes a ship target detection algorithm MC-YOLOv5s based on YOLOv5s. First, the MobileNetV3-Small lightweight network is used to replace the original feature extraction backbone network of YOLOv5s to improve the detection speed of the algorithm. And then, a more efficient CNeB is designed based on the ConvNeXt-Block module of the ConvNeXt network to replace the original feature fusion module of YOLOv5s, which improves the spatial interaction ability of feature information and further reduces the complexity of the model. The experimental results obtained from the training and verification of the MC-YOLOv5s algorithm show that, compared with the original YOLOv5s algorithm, MC-YOLOv5s reduces the number of parameters by 6.98 MB and increases the mAP by about 3.4%. Even compared with other lightweight detection models, the improved model proposed in this paper still has better detection performance. The MC-YOLOv5s has been verified in the ship visual inspection and has great application potential. The code and models are publicly available at https://github.com/sakura994479727/datas.

Sine-SSA-BP Ship Trajectory Prediction Based on Chaotic Mapping Improved Sparrow Search Algorithm.

OBJECTIVE: In this paper, we propose a Sine chaos mapping-based improved sparrow search algorithm (SSA) to optimize the BP neural network for trajectory prediction of inland river vessels because of the problems of poor accuracy and easy trapping in local optimum in BP neural networks. METHOD: First, a standard BP model is constructed based on the AIS data of ships in the Yangtze River section. A Sine-BP model is built using Sine chaos mapping to assign neural network weights and thresholds. Finally, a Sine-SSA-BP model is built using the sparrow search algorithm (SSA) to solve the optimal solutions of the neural network weights and thresholds. RESULT: The Sine-SSA-BP model effectively improves the initialized population of uniform distribution, and reduces the problem that population intelligence algorithms tend to be premature. CONCLUSIONS: The test results show that the Sine-SSA-BP neural network has higher prediction accuracy and better stability than conventional LSTM and SVM, especially in the prediction of corners, which is in good agreement with the real ship navigation trajectory.

Sonication time efficacy on Fe3O4-liquid paraffin magnetic nanofluid thermal conductivity: An experimental evaluation.

In this study, the duration of sonication efficacy on the thermal conductivity of Fe3O4-liquid paraffin nanofluid is investigated. The nanofluid is produced at 0.005, 0.01, 0.015, 0.02, 0.025 and 0.03 vol concentrations by applying two-step method. The sonication process is performed in a temperature range of 20-90 °C. The duration of sonication seems to have two important effects: On the one hand, increasing the duration of sonication breaks the nanoparticles clusters, hence distributes the nanoparticles more uniformly which in turn rises thermal conductivity. On the other hand, an excessive increase in the duration of sonication can impair nanofluid stability. The results of experimental tests proved that the optimal duration of sonication is 3 h. The optimal duration of sonication is not dependent on the nanoparticles volume fraction (φ) and temperature. It was found that at the highest temperature and φ (90 °C,0.03), the greatest thermal conductivity enhancement (28.49%) is obtained. In contrast, at the lowest temperature and φ (20 °C,0.005) the lowest thermal conductivity enhancement was obtained (2.82%).