Abstract: Aiming at the problem of low accuracy of corolla detection and position during field operation of safflower picking robots, a deep learning-based object detection and position algorithm, mobile safflower detection and position network, MSDP-Net, was proposed. For object detection, an improved YOLO v5m model was proposed. By inserting the convolutional block attention module, the model precision, recall and mean average precision were improved by 4.98, 4.3 and 5.5 percentage points, respectively, compared with those before the improvement. For spatial position, a camera-moving spatial position method was proposed, which kept the position accuracy in the best range and avoided the missed detection caused by the obstructed corolla at the same time. The experimental verification showed that the success rate of mobile camera-based positioning was 93.79%, which was 9.32 percentage points higher than that of fixed camera-based positioning, and the average deviation of mobile camera-based positioning method in X, Y and Z directions was less than 3 mm. The MSDP-Net algorithm had better performance compared with five mainstream object detection algorithms and was more suitable for the detection of safflower corolla. The MSDP-Net algorithm and the camera mobile position method were applied to the self-developed safflower picking robot for picking experiments. The indoor test results showed that among 500 replicate tests, totally 451 were successfully picked and 49 were missed, with a picking success rate of 90.20%. The field test results showed that the success rate of safflower corolla picking was greater than 90% within the selected monopoly length of 15 m.