为解决传统跌倒检测算法受光线影响，不能对独居老人实施昼夜持续检测，且误检率、漏检率高等问题，提出一种结合轻量化YOLOv5和双摄像头的跌倒检测系统。首先，通过改进YOLOv5对室内老人进行检测，定位老人在室内的位置；其次，使用单目摄像头和红外热摄像头昼夜交替地对室内老人进行跌倒检测。通过目标定位与跌倒检测系统，在大幅度提高老人跌倒检测准确率的同时，提高了模型轻量化程度，降低了跌倒误检率、漏检率。结果表明，改进后的老人跌倒检测模型漏检率低至1.6%、误检率仅1.2%，具有较好的准确性和实时性。可见该系统可以有效实现昼夜检测，解决传统算法的局限性。

To solve the problem that the traditional fall detection algorithm is affected by light and cannot continuously detect the elderly living alone day and night, and has high false detection and missed detection rates, a fall detection system combining lightweight YOLOv5 and dual cameras was proposed. Firstly, the elderly indoors were detected by improving YOLOv5, and the position of the elderly indoors was located. Secondly, a monocular camera and an infrared thermal camera were used to detect falls of the elderly indoors alternately day and night. Through the target positioning and fall detection system, while greatly improving the accuracy of fall detection for the elderly, it also improved the lightweight of the model and reduced the rate of false detection and missed detection of falls. The results show that the improved elderly fall detection model has a missed detection rate as low as 1.6% and a false detection rate of only 1.2%, with good accuracy and real-time performance. It can be seen that this system can effectively realize day and night detection and solve the limitations of traditional algorithms.