Ship name text detection method with scene priors fusion
Objective Ships are the most important carriers of waterborne transportation,accounting for over two-thirds of global trade in goods transportation.Ship names,as one of the most crucial identification pieces of information for ships,possess uniqueness and distinctiveness,forming the core elements for intelligent ship identity recognition.Achieving ship name text detection is crucial in enhancing waterway traffic regulation and improving maritime transport safety.However,in real-world scenarios,given the variations in ship size and diverse ship types,the areas of ship name text regions differ,and the aspect ratio of ship name text varies greatly across different ship types,directly affecting the accuracy of ship name text detection and increasing the likelihood of missed detections.Additionally,during ship name text detection,various elements,such as background text and patterns in the scene,can introduce interference.Existing natural scene text detec-tion algorithms do not completely eliminate these interference factors.Directly applying them to ship name text detection tasks may lead to poor algorithm robustness.Therefore,this study addresses the aforementioned issues and proposes a ship name detection method based on scene prior information.Method First,given that ship name text regions are usually fixed at the bow and two sides of the ship,this study proposes a region supervision module based on prior loss,which utilizes the correlation between the bow and the ship name text target.Through the classification and regression branches on the shared feature maps,prior information of the bow region is obtained,constructing a scene prior loss with bow correlation.During training,the model simultaneously learns the ship name text detection main task and the bow object detection auxiliary task and updates the network parameters through joint losses to constrain the model's attention to the ship name text region fea-tures and eliminate background interference.Then,a ship name region localization module based on asymmetric convolu-tion is further proposed to improve the granularity of text region localization.It achieves lateral connections between deep semantic information and shallow localization information by fusing feature layers with different scales between networks.On the basis of the additive property of convolution,three convolution kernels with sizes of 3 × 3,3 × 1,and 1×3 are used to enhance the fused feature maps,balancing the weights of the kernel region features to enrich the text edge information.Finally,a differentiable binarization optimization is introduced to generate text boundaries and realize ship name text region localization.Given that no ship name text detection dataset is publicly available,this study constructs the CBWLZ2023 dataset,comprising 1 659 images of various types of ships,such as fishing vessels,passenger ships,cargo ships,and war-ships,captured in real-world scenes such as waterways and ports,featuring differences in background,ship poses,light-ing,text attributes,and character sizes.Result To validate the effectiveness of the proposed algorithm,this study col-lected,annotated,and publicly released a real-world ship name text detection dataset CBWLZ2023 for experimental verifi-cation and compared it with eight state-of-the-art general natural scene text detection methods.Quantitative analysis results show that the proposed algorithm achieves an F-value of 94.2％in the ship name text detection task,representing a 2.3％improvement over the second-best-performing model.Moreover,ablation experiments demonstrate that the model's F-value increases by 2.3％and 0.7％after incorporating the region supervision module based on prior loss and the ship name region localization module based on asymmetric convolution,respectively.The fused model's F-value increases by 2.8％,confirming the effectiveness of each algorithm module.Qualitative analysis results indicate that the proposed algorithm exhibits stronger robustness than other methods in dealing with text of varying scales and background interference,accu-rately capturing text regions with clear boundaries and effectively reducing false positives and missed detections.Experi-mental results demonstrate that the proposed algorithm enhances ship name text detection performance.Conclusion This study proposes a ship name detection method based on scene prior information.The algorithm has two main advantages.First,it fully utilizes the strong correlation between the bow region of the ship and the ship name text region,suppressing the interference of background information in ship name detection tasks.Second,it integrates multiscale text feature infor-mation to enhance the robustness of multiscale text object detection.The proposed algorithm achieves higher detection accuracy than existing scene text detection algorithms on the CBWLZ2023 dataset,demonstrating its effectiveness and advancement.The CBWLZ2023 can be obtained from https://aistudio.baidu.com/aistudio/datasetdetail/224137.
ship name text detectionscene priori lossregional supervisionfeature enhancementasymmetric convolu-tion
万方数据
维普
