在无人机电力巡检的航拍视角下，考虑到电力设备图像特征的特殊性，针对普遍存在的小目标检测、目标点遮挡和航拍图像尺度多变引起模型漏检率上升，以及稠密检测带来计算量增加的问题，提出了一种适用于无人机边缘端的视觉检测模型EDR-YOLOv7（EVC⁃DySample⁃Reploss⁃YOLOv7）。首先，在颈部网络中引入显示视觉中心模块捕捉像素的隐性关系，解决小目标特征遗漏的问题。其次，用动态采样模块替换转置卷积实现特征点的灵活采样，降低模型计算的复杂度。最后，针对无人机视角尺度多变的问题及因局部遮挡而导致的预测框误删和偏离的问题，在损失函数中先后添加Inner-SIoU（inner-scylla intersection over union）损失项和斥力因子损失项，不断缩小训练迭代时的预测误差。经试验验证，EDR-YOLOv7相较于原模型mAP@0.5提升3.89 %的同时检测帧率提升了5.2 frames/s，最终将模型部署于Jetson XAVIER NX边缘计算机上并采用TenssorRT推理加速，在视频流检测任务中表现出色。

From the perspective of aerial photography of UAVs(unmanned aerial vehicles) during power inspections, taking into account the particularity of the image characteristics of power equipment, a visual detection model EDR-YOLOv7 is proposed suitable for the edge of UAVs to address the common problems of the ubiquitous small target detection, target point occlusion, and the increase in model missed detection rate caused by the variable scale of aerial images, as well as increased calculation amount caused by dense detection. Firstly, a display visual center module is introduced into the neck network to capture the implicit relationship of pixels and solve the problem of missing small target features. Secondly, the dynamic sampling module is used to replace the transposed convolution to achieve flexible sampling of feature points and reduce the complexity of model calculation. Finally, in order to solve the problem of variable viewing angle scale of UAVs and the problem of accidental deletion and deviation of prediction frames caused by partial occlusion, the Inner-SIoU (inner-scylla intersection over union) loss term and the repulsion factor are added to the loss function, continuously reducing the prediction error during training iterations. After experimental verification, EDR-YOLOv7 compared to the original model increases mAP@0.5 and the detection frame rate by 3.89 % and 5.2 frames/s respectively. The model is finally deployed on the Jetson XAVIER NX edge computer and accelerated by TensorRT reasoning, which performs well in video stream detection tasks.