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Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it will result in poor matching accuracy. However, roughly designing the batch size for all possible delays will result in a large computational overhead. (2) Ignoring non-stationary factors will lead to a change in optimal batch size that cannot be found as soon as possible. Therefore, this paper proposes a fine-grained, batching-based task allocation algorithm (FGBTA), considering non-stationary setting. In the batch method, the algorithm first uses variable step size to allow for fine-grained exploration within the predicted value given by the multi-armed bandit (MAB) algorithm and uses the results of pseudo-matching to calculate the batch utility. Then, the batch size with higher utility is selected, and the exact maximum weight matching algorithm is used to obtain the allocation result within the batch. In order to cope with the non-stationary changes, we use the sliding window (SW) method to retain the latest batch utility and discard the historical information that is too far away, so as to finally achieve refined batching and adapt to temporal changes. In addition, we also take into account the benefits of requesters, workers, and the platform. Experiments on real data and synthetic data show that this method can accomplish the task assignment of spatial crowdsourcing effectively and can adapt to the non-stationary setting as soon as possible. This paper mainly focuses on the spatial crowdsourcing task of ride-hailing.

In recent years, the protection and management of water environments have garnered heightened attention due to their critical importance. Detection of small objects in unmanned aerial vehicle (UAV) images remains a persistent challenge due to the limited pixel values and interference from background noise. To address this challenge, this paper proposes an integrated object detection approach that utilizes an improved YOLOv5 model for real-time detection of small water surface floaters. The proposed improved YOLOv5 model effectively detects small objects by better integrating shallow and deep features and addressing the issue of missed detections and, therefore, aligns with the characteristics of the water surface floater dataset. Our proposed model has demonstrated significant improvements in detecting small water surface floaters when compared to previous studies. Specifically, the average precision (AP), recall (R), and frames per second (FPS) of our model achieved 86.3%, 79.4%, and 92%, respectively. Furthermore, when compared to the original YOLOv5 model, our model exhibits a notable increase in both AP and R, with improvements of 5% and 6.1%, respectively. As such, the proposed improved YOLOv5 model is well-suited for the real-time detection of small objects on the water’s surface. Therefore, this method will be essential for large-scale, high-precision, and intelligent water surface floater monitoring.