## 3 Results
### Contrast Experimental Results
This shows that our proposed yolov4_RMLight_ADD model has effect on object recognition. 4. As can be seen from Table 7, our proposed model size of yolov4_RMLight_ADD is 42.5 MB, the number of parameters is 10.8 million, and the flops is 3.53 GB. The model size is 201.5 MB less than YOLOv4, the number of parameters is 83.21% less, and the flops are 88.30% less. Although the model size is 20.25 MB, 27.5 MB, 35.55 MB, and 15.4 MB more than the lightweight networks YOLOv4_tiny, Eifficientdet-d0, YOLOv5n, and YOLOv5s, respectively, the flops are similar to the lightweight networks, which means that the computational consumption on the hardware is is about the same. 5. Overall, the yolov4_RMLight_ADD model proposed in this paper has the highest precision, recall, mAP value, and F1-score among the lightweight networks; moreover, although the model size and number of parameters are more than the lightweight networks, the flops are similar. Considering the computational performance of the mobile robot device and the excellent performance of the yolov4_RMLight_ADD model in terms of recognition accuracy far above other lightweight networks, the yolov4_RMLight_ADD model is more suitable for deployment on mobile robots. In addition to detection accuracy, the time performance of the object detection algorithm on mobile robotic devices is also a key metric. We obtained the time to calculate the mAP value on different devices by deploying the object detection method on different devices, as shown in Figure 13. We also used the camera on the robot to test the real-time detection capability of the object detection algorithm, and Figure 14 shows the results of the FPS tests on different devices. Based on the experimental results of the time performance, the following conclusions can be drawn. 1. As can be seen from Figure 13, the three models consume similar times on the PC. On the Manifold 2-G device, our proposed model consumes 100.12 s, which is 53.09% lower than YOLOv4 and only 24.24 s higher than the lightweight network YOLOv4_tiny. On the Dell OptiPlex 3060 device, our proposed model consumes 534.55 s, which is 88.62% lower compared to YOLOv4 and only 434.42 s higher than the lightweight network YOLOv4. 88.62% and only 434.42 s higher than the lightweight network YOLOv4_tiny. It can be seen that on high-performance devices, the time spent by the different models is similar. On resource-limited devices, the fewer computational resources the device has, the greater the gap in computation time between different levels of algorithms. It is clear that our proposed model drastically reduces the computation time relative to the large network YOLOv4. Although not as small as that of the lightweight network YOLOv4_tiny, they are both of the same order of magnitude. Combined with the accuracy, our proposed algorithm is more advantageous. 2. As can be seen from Figure 14, our proposed model can achieve close to 30 FPS for real-time detection on pc devices, while YOLOv4 can only maintain around 20 FPS, and the lightweight network YOLOv4_tiny can achieve around 45 FPS. On the Manifold 2-G device, our proposed model achieves close to 10 FPS, while YOLOv4 only stays at around 2 FPS and the lightweight network YOLOv4_tiny achieves around 25 FPS. On the Dell OptiPlex 3060 device, our proposed model stays at around 2 FPS, while YOLOv4 is essentially 0 FPS, and lightweight network YOLOv4_tiny only reaches around 5 FPS. It is clear that the large model YOLOv4 is largely unavailable on resource-constrained devices, while our proposed model is not as good as the lightweight network, but is still generally available. Combined with the accuracy, our proposed model is very advantageous.