# FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection
## 4 Experiments
### Results on Pascal VOC 2007 Test Set  
As shown in Table 1, we compared the proposed FFESSD with several other detectors, such as R-CNN, SSD, DSSD, FFSSD methods, on the PASCAL VOC 2007 test dataset. For the two-stage detectors, R-CNN [5] is the first target detection algorithm that combines region proposals with CNN. It generates about 2000 region proposals based on the selective search [26] method, which requires a large amount of space in memory. In addition, the normalization process of region proposals makes the algorithm lose a lot of features and it features semantic information, resulting in detection accuracy of 50.2% mAP and detection efficiency of 0.07 FPS. Fast R-CNN [6] and SPP-NET [8] also have the same significant drawbacks as R-CNN [5]. In order to reduce the operation time and improve the detection accuracy of the algorithm, Faster R-CNN [8] introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. The mAP achieves 73.2% and the FPS achieves 7.0. HyperNet [30], Online hard example mining (OHEM) [31], and ION [32] also have problems of poor accuracy and real-time performance, making it difficult to satisfy the needs of real-time detection of complex large data sets. For the single-stage detectors, YOLO [9] is the first target detection algorithm to achieve real-time detection. It removes the selection of regional proposal scheme and makes the network structure simpler than the two-stage detectors. However, YOLO [9] only uses the top layer of the feature maps to detect targets of different sizes, which has great limitations on the size and location of the targets. The detection performance of mAP achieves only 63.4%, especially for small targets. In SSD [10], both low-level feature maps and top-level feature maps are used to improve the detection performance of small targets, but the low-level features have the shortcoming of insufficient semantic information. This may result in inaccurate detection of small targets. The mAP on the testing dataset achieves 77.2% with the input size 300 \(\times\) 300 and 79.5% with the input size 512 \(\times\) 512. Recently, various methods have attempted to improve the accuracy of SSD, especially for small targets, DSSD [11] uses ResNet-101 [17] instead of VGG16 [14] to achieve higher accuracy, when the input size is 300 \(\times\) 300, the detection accuracy is improved by 1.4% compared with SSD, and the DSSD achieves 81.5% mAP with the input size 512 \(\times\) 512, which is improved by 1.7% compared with SSD512. FSSD [13] inherits the network structure of SSD and introduces a lightweight and efficient Feature Fusion Module into it, which can improve the performance over SSD with just a little speed drop. R-SSD [15] improves the detection accuracy byusing the pooling operation and deconvolution operation to enhance the correlation between different feature layers in the feature pyramid structure of SSD. ESSD [33] uses two-way transfer of feature information and feature fusion to enhance the network and proposes a visual reasoning method that utilized fully the relationships between targets to assist further with target detection. DOSD [34] and feature-fused SSD [35] have improved the detection accuracy by combining feature maps with other layers. In FFESSD, we used the FFM module to fuse the feature maps of different scales in the feature pyramid, the SFE module to enhance the semantic information of the shallower layers, and the DFE module to make the deep feature maps have more details about the input image. It improved the detection accuracy and obtained a relatively fast reasoning speed, which is beneficial in practical applications. From Table 1, we know that we have achieved good results. For the 300 input model, the proposed FFESSD was 1.9% improvement in the accuracy with 79.1% mAP compared to the SSD [10], and for the 512 input model, the FFESSD achieved 81.8% mAP, which is 2.0% better than the SSD.