Mobile crowdsensing in intelligent transportation systems is a new data acquisition mode that utilizes vehicles to sense a changing traffic environment, where incentive mechanisms play a vital role in motivating vehicles to participate. Nevertheless, existing incentive mechanisms do not simultaneously consider enhancing the efficiency of task allocation, maximizing the benefits of the entire system, and balancing various costs. Therefore, a two-stage incentive mechanism, considering task aggregation, is designed to maximize the benefits of vehicles and the platform. In the first stage, each vehicle, utilizing the multiagent deep reinforcement learning algorithm, dynamically adjusts its path selection strategy to maximize its net income and then uploads the selected path information to the platform. In the second stage, this platform solves an NP-hard problem of maximizing satisfaction by combining the bid price, time consumption, and task coverage to assign tasks to the appropriate vehicles. In particular, vehicle satisfaction is measured by the analytic hierarchy process, and rewards are allocated accordingly. The proposed mechanism is compared with the state-of-the-art method, the greedy, random, and optimal algorithms. Experiments on the real trajectory dataset manifest that the proposed mechanism can fully use the platform budget to achieve the best net income, platform satisfaction, and task completion rate.

With the three-dimensional (3D) coordinates of objects captured by a sequence of images taken in different views, object reconstruction is a technique which aims to recover the shape and appearance information of objects. Although great progress in object reconstruction has been made over the past few years, object reconstruction in occlusion situations remains a challenging problem. In this paper, we propose a novel method to reconstruct occluded objects based on synthetic aperture imaging. Unlike most existing methods, which either assume that there is no occlusion in the scene or remove the occlusion from the reconstructed result, our method uses the characteristics of synthetic aperture imaging that can effectively reduce the influence of occlusion to reconstruct the scene with occlusion. The proposed method labels occlusion pixels according to variance and reconstructs the 3D point cloud based on synthetic aperture imaging. Accuracies of the point cloud are tested by calculating the spatial difference between occlusion and non-occlusion conditions. The experiment results show that the proposed method can handle the occluded situation well and demonstrates a promising performance.

With the rapid development of smart cities, vehicles equipped with various sensors can effectively sense traffic, thus forming a crowdsensing paradigm for the Intelligent Transportation System (ITS). Although mobile crowdsensing in ITS has broad application advantages, it still faces many challenges, such as single point of failure, inefficient independent task allocation, and the inability to deal with safety emergency tasks in time. To handle the above issues, we establish a decentralized ITS architecture based on blockchain and propose the concurrent tasks assignment problem proved to be NP-hard and safety emergency tasks assignment problem. Then we propose Reinforcement Learning (RL) based concurrent tasks and the safety emergency tasks assignment method, which can maximize the utility of concurrent tasks based on satisfying the requirements of safety emergency tasks. Simulation results demonstrate the effectiveness of the proposed methods.

Accurate nuclear instance segmentation and classification in histopathologic images are the foundation of cancer diagnosis and prognosis. Several challenges are restricting the development of accurate simultaneous nuclear instance segmentation and classification. Firstly, the visual appearances of different category nuclei could be similar, making it difficult to distinguish different types of nuclei. Secondly, it is thorny to separate highly clustering nuclear instances. Thirdly, rare current studies have considered the global dependencies among diverse nuclear instances. In this article, we propose a novel deep learning framework named TSHVNet which integrates multiattention modules (i.e., Transformer and SimAM) into the state‐of‐the‐art HoVer‐Net for the sake of a more accurate nuclear instance segmentation and classification. Specifically, the Transformer attention module is employed on the trunk of the HoVer‐Net to model the long‐distance relationships of diverse nuclear instances. The SimAM attention modules are deployed on both the trunk and branches to apply the 3D channel and spatial attention to assign neurons with appropriate weights. Finally, we validate the proposed method on two public datasets: PanNuke and CoNSeP. The comparison results have shown the outstanding performance of the proposed TSHVNet network among the state‐of‐art methods. Particularly, as compared to the original HoVer‐Net, the performance of nuclear instance segmentation evaluated by the PQ index has shown 1.4% and 2.8% increases on the CoNSeP and PanNuke datasets, respectively, and the performance of nuclear classification measured by F1_score has increased by 2.4% and 2.5% on the CoNSeP and PanNuke datasets, respectively. Therefore, the proposed multiattention‐based TSHVNet is of great potential in simultaneous nuclear instance segmentation and classification.

Object detection is an important task in drone vision. Since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model performance, and most existing object detectors tend to underperform in drone-vision scenes. To solve these problems, we propose a novel detector named YOLO-Drone. In the proposed detector, the backbone of YOLO is firstly replaced with ConvNeXt, which is the state-of-the-art one to extract more discriminative features. Then, a novel scale-aware attention (SAA) module is designed in detection head to solve the large disparity scale problem. A scale-sensitive loss (SSL) is also introduced to put more emphasis on object scale to enhance the discriminative ability of the proposed detector. Experimental results on the latest VisDrone 2022 test-challenge dataset (detection track) show that our detector can achieve average precision (AP) of 39.43%, which is tied with the previous state-of-the-art, meanwhile, reducing 39.8% of the computational cost.