【摘要】 无人机拍摄的图像中的物体相对较小，而对于人们的肉眼而言，即便是距离较远，也能即时提取信息，但图像分辨率和计算资源的限制使得检测较小的物体对计算机来说是一项真正具有挑战性的任务。提出了一种基于YOLOv5s的算法，具有计算资源少和准确度高的特点，可以应用于无人机等边缘检测设备。通过简化特征提取网络的深度和调整检测头特征图的大小，可以准确地识别出无人机拍摄图像中的目标。最后，网络模型的参数量减少了70%,代价是降低了一点准确度，但准确度仍在基线上提高了15.25%,即5.2个百分点。更多还原

【Abstract】 As we all know, the objects in the images taken by unmanned aerial vehicle(UAV)are relatively small, while our naked eyes are able to extract the information almost instantly, even from far away, image resolution and computational resources limitations make detecting smaller objects a genuinely challenging task for machines.We propose an algorithm based on YOLOv5s with small computational resources and high accuracy, so as to be applied to edge detection devices such as unmanned aerial vehicles.By simplifying the depth of the feature extraction network and adjusting the size of the feature map of the detection head, the target in the image taken by UAV can be accurately identified.In the end, we reduced the number of parameters by 70% at the expense of a little accuracy, while improving accuracy by 15.25%,or 5.2 percentage points, over the baseline.更多还原