<p>In order to realize fast, remote, and accurate intelligent monitoring of dust pollution in the construction of bare land areas, an intelligent monitoring method for unmanned aerial vehicles (UAVs) for environmental protection of dust pollution in the construction of bare land areas based on lightweight improvement YOLOv5 is studied. The image of the construction bare land area is remotely collected by the camera carried by the UAV, and the image enhancement algorithm for intelligent monitoring of the construction bare land area based on the bistable system is used to map the image gray value to the bipolar aperiodic pulse amplitude modulation signal. The resonance characteristics of the bistable stochastic resonance system are used to suppress the noise and enhance the image. As a recognition sample for the intelligent recognition model of fugitive dust images in construction bare land areas, which is based on the lightweight-improved YOLOv5. This model uses the improved coordinated attention, and the backbone network of coordinated attention (CA) mechanism extracts the important features in the monitoring images of bare areas before and after the increase and uses the weighted bidirectional feature pyramid improved by the network neck to achieve multi-scale feature fusion. Subsequently, the fusion-extracted feature map is used to identify the boundary box of dust targets and realize the intelligent monitoring of dust environmental protection UAV in the construction of bare areas. Through testing, owing to the lightweight improvement of the YOLOv5 network, the intelligent monitoring results of unmanned aerial vehicles for fugitive dust and environmental protection in the construction bare land area are accurate and efficient.</p> Graphical Abstract <p></p>