Active learning (AL) and semi-supervised learning (SSL) both aim to reduce annotation costs: AL selectively annotates high-value samples from the unlabeled data, while SSL leverages abundant unlabeled data to improve model performance. Although these two appear intuitively compatible, directly combining them remains challenging due to fundamental differences in their frameworks. Current semi-supervised active learning (SSAL) methods often lack theoretical foundations and often design AL strategies tailored to a specific SSL algorithm rather than genuinely integrating the two fields. In this paper, we incorporate AL objectives into the overall risk formulation within the mainstream pseudo-label-based SSL framework, clarifying key differences between SSAL and traditional AL scenarios. To bridge these gaps, we propose a feature re-alignment module that aligns the features of unlabeled data under different augmentations by leveraging clustering and consistency constraints. Experimental results demonstrate that our module enables flexible combinations of SOTA methods from both AL and SSL, yielding more efficient algorithm performance.

Using deep learning to detect road damage can significantly improve the effectiveness of road maintenance. To promote the development of road damage detection, we construct a high-resolution road damage data set named Asphalt Road Surface Disease Dataset(ARSDD), comprising 2297 images used in a real-world project. The annotation process is under the guidance of the road maintenance department, and it has more accurate labels and appropriate damage types. Most current road damage detection models are anchor-based, where one anchor corresponds to one sample. Therefore, these models are primarily limited by the setting of pre-defined anchors. Road damages have more extreme aspect ratios and scales than natural objects, and the general settings of anchors are inappropriate for road damage. In this paper, we propose a road damage detection model based on an improved adaptive training sample selection strategy, which can reduce manual anchor settings and is suitable for road damage detection. Moreover, as slight road damages tend to lose information during down-sampling, a cross-layer attention feature pyramid network is designed to compensate for this degradation in the spatial dimensions. While testing the ARSDD dataset, we find that the evaluation criterion for general object detection is unsuitable for road damage detection and propose a new post-processing method and diagonal-based evaluation criterion according to the characteristics of road damage. We validate our model using the 2018 Road Damage Dataset and our proposed dataset, and the results demonstrate the superiority of our model in road damage detection.Note to Practitioners—This paper was motivated by the problem of road damage detection, which is the key of intelligent road maintenance system. We comprehensively analyze the shortcomings of current publicly available content about road damage detection, including detection datasets, algorithms and evaluation metrics. Correspondingly, we first construct a road damage dataset from the real-world project, which contains six common categories of disease and is annotated under the guidance of the road maintenance department. Then we propose a detection model based on computer vision technology to improve the accuracy of damage detection. Finally, we propose a new post-processing method and a diagonal-based evaluation criterion based on the detection boxes. Any practitioner working on pavement inspection systems can use our released dataset and method to build a better-performing automated inspection system. In future research, we will try more refined inspection schemes and evaluation indicators, such as meshing the road surface images and evaluating the quality of the road based on the inspection results of each mesh.

The three-phase four-leg four-wire three-level (3P4L4W 3L) inverters are widely used in mediate-to-high power applications for supplying power to unbalanced and/or nonlinear ac loads. To save filter inductance, a neutral inductor is often inserted in the neutral wire for reducing the ripple of the common-mode (CM) current at the high switching frequency. The filter inductance and the neutral inductance are yet not specifically designed for the 3P4L4W 3L inverter. For obtaining the minimum required inductance, a complete design methodology is proposed for calculating the ac filter inductance and neutral inductance. Finally, simulation results are presented to verify the feasibility the proposed optimized design for the filter inductors and the neutral inductor.

The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both data-driven and model-based algorithms. The time-varying constant velocity model is integrated into the Gaussian process (GP) of online learning to improve the performance of GP prediction. This integration is further combined with a generalized probabilistic data association algorithm to realize multi-target tracking. Through the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the data-driven GP motion tracker.

The widespreadPomacea canaliculataandPomacea maculatain North America and Asia have caused significant adverse effects on the local ecological environment and residents’ health. Timely knowledge of the distribution of eggs from the twoPomaceaspp. in a certain region can effectively reduce the cost of treatment and improve prevention effectiveness. Most of the existing methods are only able to identify eggs from the twoPomaceaspp. or detect them in specific but not natural environments while they cannot achieve good results in the face of a complex real-world scene. This letter proposes a model for detecting eggs from the twoPomaceaspp. based on dynamic convolution and multiscale feature fusion. The model can identify and locate the eggs of the twoPomaceaspp. effectively. At the same time, we combined the proposed model with scale invariant feature transform (SIFT) algorithm to design a system for counting eggs of the twoPomaceaspp., which can automatically identify the eggs in the actual natural environment and alleviate duplicate counting caused by image acquisition. Besides, we also built a dataset of 20 000 images ofPomacea canaliculataeggs andPomacea maculataeggs from unmanned aerial vehicle (UAV) aerial photography. Experimental results showed that the proposed deep learning model has a better performance than others, and the proposed computer vision system can be successfully applied to supportPomaceaspp. disease management.