Reliable detection of objects is a crucial requirement to improve the safety of the visual Internet of Vehicles (V-IoVs). The resemblance of objects to each other or the background makes the detection of objects difficult. The traditional connectionist artificial intelligence (All-based object detection model is a potential approach to increase detection performance, but it has poor interpretability and high computational complexity. Edge intelligence has demonstrated a considerably good balance between efficiency and computation complexity, integrating multi-access edge computing and AI. In this article, an edge AI framework is designed to perform the object detection task, and a novel abductive learning algorithm is proposed to realize the interpretability and robustness of AI in the V-IoV system. Based on the you only look once (YOLO) classifier, the abductive model is used to learn from the data, which combines symbolic and connectionist AI. Moreover, blockchain is developed for model sharing. Compared to the state of the art, simulation results show the high accuracy of the proposed algorithm. Moreover, the proposed approach has interpretability and strong robustness.

中文翻译：

---

基于区块链的车联网中物体检测的边缘智能：符号人工智能和连接主义人工智能的融合


可靠的物体检测是提高视觉车联网 (V-IoV) 安全性的关键要求。物体彼此或背景的相似性使得物体的检测变得困难。传统的联结人工智能（All-based object discovery model）是一种提高检测性能的潜在方法，但其可解释性较差，计算复杂度较高。边缘智能在效率和计算复杂度之间表现出了相当好的平衡，集成了多访问边缘本文设计了一个边缘人工智能框架来执行目标检测任务，并提出了一种新颖的溯因学习算法，以实现基于“you only view”的 V-IoV 系统中人工智能的可解释性和鲁棒性。一次（YOLO）分类器，使用溯因模型从数据中学习，它结合了符号和连接主义人工智能。此外，与现有技术相比，区块链是为了模型共享而开发的，模拟结果表明所提出的方法具有很高的准确性。此外，该方法具有可解释性和较强的鲁棒性。