In the context of autonomous driving environment perception, multi-modal fusion plays a pivotal role in enhancing robustness, completeness, and accuracy, thereby extending the performance boundary of the perception system. However, directly applying LiDAR-related algorithms to radar and camera fusion leads to significant challenges, such as radar sparsity, absence of height information, and noise, resulting in substantial performance loss. To address these issues, our proposed method, SparseFusion3D, utilizes a dual-branch feature-level fusion network that fully models sensor interactions, effectively mitigating the adverse effects of radar sparsity and noise on modality association. Additionally, to enhance modal correlations and accuracy while alleviating radar point cloud sparsity and measurement ambiguity, we introduce MSPCP, which compensates for point cloud offset. Moreover, we integrate Radar Painter to leverage image information and further enhance MSPCP. SparseFusion3D exhibits competitive performance compared to previous radar-camera fusion models, achieving approximately 1.5x inference speedup with similar performance to dense query methods, while also improving by 20.1% compared to the baseline approach.

FOD has always been a major concern for airport security because FOD on airport runways can cause significant damage to aircraft.¹ To avoid the risk of loss of life or property caused by FOD, FOD detection has become a significant task. At present, there are two types of FOD detection methods: millimeter wave radar-based and vision-based.¹ Although millimeter wave radar has the ability to detect and locate FOD, the point cloud of millimeter wave radar is sparse and lacks semantic information to classify the type of FOD, which cannot satisfy the airport’s demands at this stage.

At present, there is no uniform definition of annotation schemes for causal extraction, and existing methods are limited by the dependence of relations on long spans, which makes complex sentences such as multi-causal relations and nested causal relations difficult to extract. To solve these problems, a head-to-tail entity annotation method is proposed, which can express the complete semantics of complex causal relations and clearly describe the boundaries of entities. Based on this, a causal model, RPA-GCN (relation position and attention-graph convolutional networks), is constructed, incorporating GAT (graph attention network) and entity location perception. The attention layer is combined with a dependency tree to enhance the model’s ability to perceive relational features, and a bi-directional graph convolutional network is constructed to further capture the deep interaction information between entities and relationships. Finally, the classifier iteratively predicts the relationship of each word pair in the sentence and analyzes all causal pairs in the sentence by a scoring function. Experiments on SemEval 2010 task 8 and the Altlex dataset show that our proposed method has significant advantages in solving complex causal extraction compared to state-of-the-art methods.

To address the defects of the salp swarm algorithm (SSA) such as the slow convergence speed and ease of falling into a local minimum, a new salp swarm algorithm combining chaotic mapping and decay factor is proposed and combined with back propagation (BP) neural network to achieve an effective prediction of tool wear. Firstly, the chaotic mapping is used to enhance the formation of the population, which facilitates the iterative search and reduces the trapping in the local optimum; secondly, the decay factor is introduced to improve the update of the followers so that the followers can be updated adaptively with the iterations, and the theoretical analysis and validation of the improved SSA are carried out using benchmark test functions. Finally, the improved SSA with a strong optimization capability to solve BP neural networks for the optimal values of hyperparameters is used. The validity of this is verified by using the actual tool wear data set. The test results of the benchmark test function show that the algorithm presented has a better convergence speed and solution accuracy. Meanwhile, compared with the original algorithm, the R² value of the part life prediction model proposed is improved from 0.962 to 0.989, the MSE value is reduced from the original 34.4 to 9.36, which is a 72% improvement compared with the original algorithm, and a better prediction capability is obtained.