# Intelligent Detection of Parcels Based on Improved Faster R-CNN
## 1 Introduction
Subsequent researchers improved YOLO and SSD and successively proposed a series of \"one-size-fits-all\" detection algorithms such as YOLO-V2, YOLO-V3, FSSD, RSSD, DSSD and RetinaNet to further improve detection accuracy [13; 14; 15]. In addition to the above two types of detection algorithms, other researchers treat object detection as a decision problem and iterate the bounding box through an attention  
Figure 1: Parcel picking: (a) automated; (b) manual. mechanism aiming at accurate object localization. For example, [PERSON] et al. [16] proposed the AttentionNet that repeatedly shifts the positions of the upper left and lower right corners of the enclosing frame to obtain detection results. [PERSON] et al. [17] used augmented learning to propose a top-down target search strategy that utilizes a larger envelope to initially localize the object and then stepwise shrinks the envelope to enclose the detected object perfectly. [PERSON] et al. [18] utilized reinforcement learning to focus on an image's region that contains more information, which is then exploited for object detection. However, such methods are still at an early stage, and further research is required. Due to CNN's outstanding performance in object detection, several researchers have successively applied CNN for robotic sorting. [PERSON] et al. [19] combined deep belief neural networks and evolutionary algorithms to improve the accuracy and efficiency of robotic sorting systems. [PERSON] et al. [20] proposed a multimodal deep CNN detection model that employed fused color image and depth information to determine the target's grasping position using a five-dimensional structured grasping bounding box. [PERSON] et al. [21] combined deep CNN and geometric features of the grasping target to obtain the best graspable region of the sorting target, while a structured penalty term optimized the connection between multiple modes to improve grasping efficiency. [PERSON] et al. [22] suggested a fully convolutional visual grasping site detection network based on directed anchor frames. This method combined scene depth information and grasping site detection results to achieve real-time target grasping sites detection. [PERSON] et al. [23] introduced a grasping system for grasping and identifying known and novel targets in cluttered environments. This method affords pixel-level segmentation of target grasping candidate regions by combining ResNet and fully convolutional neural networks and then selects the best grasping location for the target employing an optimization strategy. [PERSON] et al. [24] suggested a neural network complex scene robot parcel-picking method, which extracts more detailed features of the parcels by fusing multi-layer shallow feature maps with the final feature maps. Then, a cascaded convolutional optimal picking location network based on key points performs real-time prediction estimation of the optimal picking location of parcels. Finally, the robot picking process combines depth information of the optimal parcel picking frame and scene and combines it with the target's pose estimation based on 3D information. [PERSON] et al. [25] used two tandem deep CNNs as the object and functional region detectors, respectively, followed by a conditional random field for the post-optimization process determining the target's sorting location. [PERSON] et al. [26] used deep CNN to design a closed-loop control appropriate for robot sorting and then trained the CNN to learn the distance function for accurate target grasping pose obtaining better sorting results. In summary, a crucial step of robot parcel picking is accurately detecting the parcel's position within the image. Although current methods allow robots to achieve object grasping, the complex working environment of logistics centers, dense stacking of parcels, occlusion, and surface interference (see Figure 2) still pose open challenges for direct parcel detection. Thus, to solve the problem of parcel detection, this work develops an improved Faster R-CNN model.