# An Enhanced Feature Pyramid Object Detection Network for Autonomous Driving
## 4 Application in Autonomous Driving System  
To further test the model practicality in real autonomous driving scenes, we installed the proposed EFPN in an autonomous driving cart for freight transportation inside a factory. The testing shows that EFPN can effectively provide surrounding object information for autonomous vehicles.  
EFPN works in the perception module of autonomous driving cart. The perception module first feeds the data obtained from binocular vision camera into EFPN for object detection. Then, the results are validated by laser radar information. Next, all object information in current frame is wrapped into a ROS message and sent to decision module for further path planning and vehicle control. More broadly, EFPN supports plenty of functions such as obstacle avoidance, trajectory following and automatic parking in autonomous driving cart.  
Three of the most crucial measures for object detection algorithms in autonomous driving cart are accuracy, real-time performance and vision range.  
For accuracy, to satisfy production demand, the autonomous driving cart needs full-time work, including daytime, nighttime and extreme weather such as rain and snow. Besides, there are many special engineering vehicles in the factory whose appearances are significantly different from normal cars. Moreover, closely parked custom vehicles along roadside are always viewed as overlapped objects. The aforementioned challenges increase the detection difficulty and put forward higher requirements on the accuracy of object detection algorithm. The proposed deep learning model EFPN ensures the capacity of robustly detecting complex objects. For better adapting to the factory scene, we constructed a factory object detection dataset based on Pascal VOC, KITTI, and special vehicles that appear in the factory. Meanwhile, we defined a new object class _cone_ for further traffic control and parking management. Overall, the factory dataset includes 21392 images, and four classes, which are _car_, _person_, _cone_ and _bicycle_. We trained EFPN on the factory dataset using the same setting as KITTI.  
For real-time performance, EFPN operates up to 16 fps cooperating with the 10 Hz vehicle controller, adequately ensuring the safety of autonomous driving cart.  
For vision range, due to the restricted limit in the factory, the speed of the autonomous cart is lower than 10 km/h. The max braking distance is less than 0.5 m, which results in 15 m visualization demand of object detection system. Therefore, the proposed EFPN with the small object detection ability within 80 m in daytime and 50 m at nighttime well satisfies the vehicle reaction requirement within safe distances.  
Figure 6 shows the real-time detection results of EFPN under some challenging scenarios. Until now, the autonomous driving cart has been operated in the factory for several months. The practical applicability of EFPN has been effectively verified.  
Figure 6: Real-time detection results in different lighting conditions and weathers in factory scenarios. (**a**) Detection results on sunny days; (**b**) Detection results on rainy days; (**c**) Detection results in the nighttime.