# Intelligent Detection of Parcels Based on Improved Faster R-CNN
## 4 Conclusions  
Parcel detection is a very important step to achieve intelligent logistics. As the parcels in logistics centers often present challenges, such as dense distribution and mutual occlusion, which leads to different degrees of false detection and misdetection in current parcel detection methods. To solve this problem, this paper proposed a novel parcel detection method with improved Faster R-CNN. The main conclusions are as follows.  
1. First, considering the problems of dense parcel stacking and mutual occlusion leading to false and missed detection, we constructed an edge detection branch using a Sobel filter to incorporate edge information into the network in the form of an additional auxiliary network. This strategy improves the model's detail processing at the edges while accelerating the network's convergence, allowing the model to learn more edge features of the parcels.
2. Second, we designed a self-attention ROI Align module for the problem of misaligned target features due to rounding in ROI Pooling. This module uses an attention mechanism to filter and enhance the features and uses bilinear interpolation to eliminate the misalignment of features caused by rounding operations.
3. Finally, we conducted a series of experiments on the parcel dataset and compared our approach with state-of-the-art deep learning methods (Faster R-CNN, YOLO-V3, SSD, and RetinaNet). In the parcel dataset, our method achieved 96.89% of Re, 98.76% of Pr, 97.72% of AP (0.5) and 22.83 of FPS, meeting the requirements for efficient and accurate parcel detection in natural industrial complex environments. To verify the generality of the method in this paper, we conducted comparative experiments on the public dataset SKU110K, and our method achieved 62.03% of Re, 60.65% of Pr, 61.01% of AP (0.5) and 24.47 of FPS. The experimental results show that our method is not only applicable to the detection of parcels but also to the detection of other dense objects.  
The parcels in this paper are all square objects with regular shapes. In future research work, we will focus on the detection of irregular parcels in combination with the instance segmentation algorithm to further extend the application scope. In addition, parallel training strategies, depth-separable convolution and model pruning are considered to optimize the complexity of the algorithm so that it can be trained and predicted on a low-configuration computer, while improving the efficiency of the algorithm to achieve real-time detection.  
Investigation, Y.Z.; Validation, Q.Z.; Writing--original draft, K.Z.; Writing--review & editing, Y.W. All authors have read and agreed to the published version of the manuscript. The authors would like to acknowledge the support of the National Natural Science Foundation of China--Key Project 61733004. The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Re (\%)** & **Pr (\%)** & **AP (0.5) \%** & **FPS** \\ \hline Faster R-CNN & 60.39 & 55.97 & 58.70 & 24.03 \\ SSD & 48.28 & 53.61 & 50.27 & 35.71 \\ YOLO-V3 & 58.44 & 55.19 & 56.59 & 83.33 \\ RetinaNet & 60.42 & 58.99 & 59.73 & 23.84 \\ Ours & 62.03 & 60.65 & 61.01 & 24.47 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative comparison results on public dataset.