# EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems
## 1 Introduction
Algorithms derived from computer vision and AI are employed to decode labels, barcodes, and QR codes, read any text, and identify objects [8]. Regarding the recognition of boxes, numerous academic research groups have conducted studies that achieved significant advancements in the identification of packing structures [1; 4; 9; 10; 11; 12; 13]. A multi-step image processing pipeline was suggested by [PERSON] et al. for automated packaging structure recognition [1]. This pipeline begins with the localization and identification of logistics transport units, followed by the convolutional neural network (CNN)-based and image processing techniques to segment visible side faces. Packaging units are consolidated and classified to produce a single RGB image that shows the structure of each unit. In [4], [PERSON] and colleagues suggested using a deep learning instance segmentation model to detect and segment transport units in an input image, with the aim of recognizing and counting packages. This is followed by intra-unit segmentation to identify base pallets, transport unit sides, and package units. Information consolidation entails the allocation of packaging units to certain sides of transport units, refining the segmentation, and calculating the number of packages based on adjusted sizes. This process leads to the precise identification of packing structures inside transport units. [PERSON] and [PERSON] [9] proposed a hybrid approach that aims to perform the real-time instance segmentation of packages using 2D distance maps by integrating both deep learning and digital signal processing techniques. [PERSON] et al. [10] suggested an approach for intelligent de-palletizing systems, which uses computer vision to recognize containers. The approach uses geometric-based structural elements, non-maximum suppression, better mean-shift clustering, and line merging. [PERSON] et al. [11] suggested a method that involves reconstructing the shapes of undamaged and damaged parcels using RGB photos. It utilizes the CubeRefine R-CNN architecture, which combines 3D bounding box estimates with iterative mesh refinement. This approach allows detecting mistreatment and enables tampering detection. The study in [12] discusses automating the unloading of containers and suggests a technique that combines a Mask-RCNN algorithm, which is based on deep learning, with 3D depth measurements to detect targets. In [13], TetraPackNet (a unique model) is presented for segmenting objects by four vertices rather than bounding boxes or pixel masks. The model is used for recognizing the structure of logistics packaging. Considering the related work above, there is no work similar to our research. Our research is important and difficult in that it considers the task of identifying specific types of boxes. These boxes have nearly identical shapes and are hard to separate into different layers stacked within them. For instance, in Figure 1, we can see that box 13 and box 04 have similar shapes, leading to the misclassification of box types by a deep learning model. This paper proposes a technique that utilizes vision-based package recognition. Our task is two-fold: (1) identify an empty location within the usable storage area to load the box; and (2) identify the category and the number of boxes. Although current detection approaches employing deep learning techniques provide high precision, they encounter many constraints, such as an excessive number of parameters, poor system runtime rates, and the occurrence of false positives. Hence, this study suggests an ELAN and GhostConv-based YOLO network (EGCY-Net) for stacked package detection that applies YOLOv7 [14] and combines ELAN design techniques with GhostConv and other operations. The following summarizes the main contributions of this study. * To the best of our knowledge, we are the first to apply one-stage detection to tackle the task of identifying types of boxes and counting the number of boxes.