# Intelligent Detection of Parcels Based on Improved Faster R-CNN
## 1 Introduction  
With the development of modern industrial robots, an increasing number of logistics aspects are being replaced by robots, i.e., parcel transportation, warehousing, packaging, picking, and processing (see Figure 0(a)). Parcel picking is a crucial task, and efficient parcel picking can reduce logistics costs and efficiency. The traditional manual parcel picking method is still widely used in most logistics transit centers (Figure 0(b)). However, this method is both time-consuming and labor-intensive and is not suitable for the modern logistics industry. Therefore, fast and accurate detection of parcels in logistics transit centers is of great interest. To achieve that, automated industrial robots are exploited, with parcel detection being a critical process for parcel picking [1], as industrial robots must first correctly detect parcels within the image. Parcel detection is an object detection task within the field of computer vision. Recently, the rapid development of convolutional neural networks (CNN) and computer hardware afforded deep learning to become an essential asset for object detection [2], involving two-staged and one-staged methods. Two-staged algorithms are based on regional recommendation, i.e., in the original image the candidate region of the object to be detected is generated, and then on each candidate region, object identification and location are performed. In 2013, [PERSON] et al. [3] proposed the first region-recommendation-based object detection algorithm entitled R-CNN, which generated 2000 object candidate regions using selective search and then employed CNN to extract features from the candidate regions. However, the R-CNN must scale and crop each candidate region to the same size for feature extraction, causing severe object deformation in the original image and imposing redundant calculations that cannot be accelerated using GPUs, resulting in high computational costs. To address this problem, [PERSON] et al. [4] proposed SPP Net, which pools images of arbitrary size into a fixed-length image representation and solves the object deformation problem caused by cropping and scaling operations in R-CNN. Nevertheless, SPP Net still requires feature extraction for each candidate region and imposes a significant computational burden. On this basis, in [5], the authors proposed the Fast R-CNN variant, where a CNN is applied to the original image to obtain the feature map and links the candidate regions to the feature map to realize the sharing of candidate regions to the deep convolutional layer. This strategy solves the feature extraction requirement per candidate region, significantly reducing the computational burden. Despite this advantage, Fast R-CNN still requires pre-generating the object candidate boxes, limiting the overall algorithm's efficiency. Subsequently, [PERSON] et al. [6] developed the Faster R-CNN algorithm, involving a new candidate region generation network (RPN), solving the candidate box pre-generation problem and affording GPU acceleration. This upgrade resulted in qualitative accuracy and performance improvement. Spurred by the advantages of Faster R-CNN, several detection algorithms such as R-FCN, FPN, R-FCN-3000, and Cascade R-CNN [7; 8; 9; 10] further improved accuracy and detection speed. One-staged algorithms rely on regression and do not require generating the target's candidate region, as these methods directly employ regression for object recognition and localization. The \"one-blade flow\" detection algorithms are represented by the YOLO and SSD families. In 2015, [PERSON] et al. [11] proposed YOLO, which directly solves the object detection problem as a regression task without generating candidate regions. Despite YOLO being very fast in detection, it is less robust and generalizable for small targets. [PERSON] et al. [12] proposed SSD to address the above problems, which extracts feature maps of different scales and uses a priori frames of different scales and aspect ratios.