论文标题
埃德加：AI实时嵌入枪声
EDGAR: Embedded Detection of Gunshots by AI in Real-time
论文作者
论文摘要
电子射击计数器允许Armourer基于定量测量，提高可靠性，降低事故频率并降低维护成本，进行预防和预测性维护。为了应对低销售市场和增加定制的市场压力，我们旨在通过机器学习以通用的方式解决射击检测和射击计数问题。 在这项研究中，我们描述了一种方法，允许人们通过仅需在时间序列中发射的镜头数量来构建以最少的标签工作来构建数据集。据我们所知，这是第一个基于从标签比例学习的技术提出一种技术的研究，该技术能够利用这些薄弱的标签来得出一个实例级别的分类器，能够解决计数问题和更一般的歧视问题。我们还表明，该技术可以部署在严格的微控制器中，同时仍提供艰苦的实时推理。我们根据最先进的无监督算法评估了我们的技术，并显示出很大的改进，这表明来自弱标签的信息已成功杠杆化。最后，我们评估了针对人类生成的最新算法的技术，并表明它提供了可比的性能，并在某些离线和现实的基准测试中显着优于它们。
Electronic shot counters allow armourers to perform preventive and predictive maintenance based on quantitative measurements, improving reliability, reducing the frequency of accidents, and reducing maintenance costs. To answer a market pressure for both low lead time to market and increased customisation, we aim to solve the shot detection and shot counting problem in a generic way through machine learning. In this study, we describe a method allowing one to construct a dataset with minimal labelling effort by only requiring the total number of shots fired in a time series. To our knowledge, this is the first study to propose a technique, based on learning from label proportions, that is able to exploit these weak labels to derive an instance-level classifier able to solve the counting problem and the more general discrimination problem. We also show that this technique can be deployed in heavily constrained microcontrollers while still providing hard real-time (<100ms) inference. We evaluate our technique against a state-of-the-art unsupervised algorithm and show a sizeable improvement, suggesting that the information from the weak labels is successfully leveraged. Finally, we evaluate our technique against human-generated state-of-the-art algorithms and show that it provides comparable performance and significantly outperforms them in some offline and real-world benchmarks.
