由于夜间光照条件不佳，场景复杂，人体形态可能被遮挡，运动中的物体容易产生鬼影等因素的影响。因此，视频监控在对夜间行人跌倒的目标识别与目标检测任务中，通常存在检测速率不佳、检测错误及无法检测等问题。为了解决此类问题，通过对原始的YOLOv8（you only look once version 8）基础模型进行了改进，即在YOLOv8的Neck部分引入了CCFM（cross-scale feature fusion module）融合模块，从而形成一种新的检测模型CCFM-YOLOv8n，以此来增强模型对动态的特征尺度和小目标的检测能力，并且保持较高的检测速度。同时，将全局注意力机制（global attention mechanism, GAM）添加至模型的Backbone部分，以提升深度神经网络综合性能。最后，改进模型引入MPDIoU（minimum point distance intersection over union）损失函数，以增强网络模型的边框回归能力。实验结果表明，改进算法对夜间跌倒目标的检测准确率达到了96.1%，召回率也提升至92.4%，相较于YOLOv8n在mAP@0.5：0.95指标上提升了3.8个百分点，参数量下降19.7个百分点。这充分表明在视频监控对夜间行人跌倒检测的实际应用中，改进模型比原模型更具优势。

Due to poor lighting conditions at night, complex scenes, potential occlusion of human forms, and the tendency of moving objects to produce ghosting effects, video surveillance often faces challenges such as suboptimal detection rates, detection errors, and missed detections in the tasks of identifying and detecting nighttime pedestrian falls. To address these issues, the original YOLOv8(you only look once version 8) base model, the cross-scale feature fusion module (CCFM) was introduced into the Neck section of YOLOv8, resulting in a new detection model called CCFM-YOLOv8n. To enhance the model's ability to detect dynamic feature scales and small targets while maintaining high detection speed. Additionally, the global attention mechanism (GAM) was incorporated into the Backbone section of the model to improve the overall performance of the deep neural network. Finally, the improved model introduced the minimum point distance intersection over union (MPDIoU) loss function to enhance the bounding box regression capability of the network model. Experimental results demonstrate that the improved algorithm achieves a detection accuracy of 96.1% for nighttime fall targets, with the recall rate increasing to 92.4%. Compared to YOLOv8n, the mAP@0.5:0.95 metric improved by 3.8 percentage points, while the parameter count decreased by 19.7 percentage points. This fully demonstrates that the improved model offers greater advantages over the original model in the practical application of video surveillance for nighttime pedestrian fall detection.