In order to accurately and efficiently identify and locate the bird droppings on small photovoltaic power station, the improved YOLOv5 model is carried on the Raspberry Pi to form a bird droppings detection system of photovoltaic power plants. The system reduced the threshold of confidence to identify all suspicious bird droppings, identified and partitioned single photovoltaic panels, and increased the confidence threshold to accurately detect suspicious bird droppings in photovoltaic panels. In order to make the YOLOv5 algorithm more suitable for detection, the pyramid split attention was integrated in the algorithm. The small target detection layer was added and the original pooling operation was replaced by SoftPool. In the test set, the mAP_0.5 of PV-YOLOv5 model identified for photovoltaic panels was 96.78%, which was 2.35 percentage points higher than that of Faster-RCNN. The mAP_0.5 of NF-YOLOv5 for bird droppings recognition was 94.12%, which was 5.8 percentage points higher than the original YOLOv5 model.