# An Enhanced Feature Pyramid Object Detection Network for Autonomous Driving
## 3 Experiments on Open Datasets
### Object Detection Results on Pascal VOC  
To evaluate the performance of EFPN and the two new subnets proposed in this paper, we compared them with other related object detection algorithms on VOC07+12 dataset. Results are shown in Table 1. Note that Approaches (b)-(d) and (i) were implemented and tested on the same platform as the proposed EFPN, while the results of Approaches (e)-(h) and (j) were from their corresponding publications. Compared with baseline (d), the controlled experiments (b) and (c), which separately replace one part of the original FPN model, prove the validity of the proposed enhanced feature extraction subnet and adaptive parallel detection subnet. Since the two subnets enhance the feature by applying weight distribution of the entire feature map and feature supplementary of each RoI, respectively, they do not overlap or inhibit each other. This is evidenced by the increased accuracy of the merged method (a) compared with either single module (b) or (c). Overall, among all related methods, the proposed EFPN (a) holds the highest mAP and highest AP in car, person and train. Adaptive parallel detection subnet assembles two new module ACE and PDB. We designed ablation studies to quantify the effect of the two modules on the VOC07 dataset. The results are reported in Table 2. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Approach** & **Backbone** & **Car** & **Person** & **Bus** & **Bike** & **Motorbike** & **Train** & **mAP** \\ \hline (a) EFPN & Res101 & 88.7 & 85.4 & 88.4 & 86.8 & 88.2 & 88.0 & 81.6 \\ (b) FPN with enhanced & Res101 & 88.6 & 85.4 & 86.7 & 86.6 & 89.0 & 86.4 & 81.3 \\ feature extraction subnet & & & & & & & & \\ (c) FPN with adaptive & Res101 & 88.5 & 84.4 & 88.0 & 88.5 & 86.4 & 86.9 & 81.4 \\ parallel detection subnet & & & & & & & & \\ (d) FPN & Res101 & 88.2 & 84.7 & 86.9 & 85.5 & 85.5 & 87.2 & 81.1 \\ (e) SSD 513 & Res101 & 88.1 & 83.0 & 88.2 & 87.6 & 87.5 & 87.2 & 80.6 \\ (f) DSSD 513 & Res101 & 88.7 & 83.7 & 89.0 & 86.2 & 87.5 & 85.7 & 81.5 \\ (g) R-FCN & Res101 & 88.5 & 81.2 & 86.8 & 87.2 & 79.9 & 85.9 & 80.5 \\ (h) MR-CNN & VGG & 85.9 & 76.4 & 88.0 & 84.1 & 85.0 & 85.0 & 78.2 \\ (i) Faster R-CNN & Res101 & 85.3 & 75.4 & 85.1 & 80.7 & 80.9 & 85.3 & 76.4 \\ (j) ION & VGG & 85.1 & 74.4 & 85.4 & 83.1 & 82.2 & 84.2 & 75.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Object detection results evaluated on VOC07+12. The increased detection accuracy in (b)-(e) compared with baseline (a) proves that adding context to a tight-fitting RoI is beneficial. However, different AP improvement of different context expansion ratio indicates that the expansion range has different effects on various objects. If we simply introduce a fixed context expansion ratio for all objects, it may result in mismatching for different object requirements. In the case that the detection branches are all in parallel design, the performance of our proposed ACE (i) is better than all fixed context expansion methods, which proves the effectiveness of our adaptive strategy. We compared four detection architectures in context integration networks. _Share_ in (a) denotes the normal object detection network, which shares the fully connection layer of both classification and regression without context embedding. _Merge_ is the context embedding method proposed in [10], where the features extracted from RoI and ceRoI after RoI pooling are paired and merged by a \(3\times 3\) convolution, and then same as _Share_ design. We tested a fixed context expansion ratio of 0.5, which is the same as [10] in (f). The result shows it slightly improves by 0.4 mAP. Nevertheless, we found that the accuracy is reduced when applying the adaptive context ratio to (j). One cause may be that the inconsistent expansion ratio leads to variable feature representation of ceRoI, thus it is hard to train a convolution kernel to merge the various feature pair. _Concat_ in (h) reproduces the context embedding design proposed in [20].