## 2 Related Work
### Research on Model Lightweight and Improvement  
In industrial production and processes, real-time requirements often arise for the detection of objects/defects. Real-time edge devices installed at a production site usually have limited computational capacity, and computational costs make it difficult to deploy large and complex networks [50; 51; 52]. A model for real-time object detection tasks needs to take into account memory requirements and computational costs and find a balance with detection performance. Therefore, lightweight networks like SqueezeNet [53], MobileNetV1 [54], ShuffleNet [55], and GhostNet [56] have been developed that balance accuracy with speed. In addition, the YOLO series of algorithms have been developed for this purpose. SqueezeNet puts forth a novel network architecture, designated the fire module, which is designed to achieve satisfactory accuracy with a reduced number of parameters. In its place, a \(1\times 1\) convolution is employed, and the channel of the \(3\times 3\) convolution input is reduced. The next stage of the network performs the downsampling. SqueezeNext [57] employs a two-stage compression process to achieve a substantial reduction in the number of channels. The use of a separable \(3\times 3\) convolution allows for a further reduction in model size. In the MobileNet series [54; 58], the Deep Separable Convolution (DSConv) module has been proposed. By lowering the parameter count, the model is smaller, and the calculations are less complex. It is well-suited to integration into mobile devices. In ShuffleNet, the network is extended by the incorporation of a group convolution module based on DSConv and a channel shuffling. In ShuffleNetV2 [59], channel splitting is employed in lieu of groupwise convolutions, with the objective of reducing the number of network parameters. This approach is intended to mitigate the potential for a large number of group convolutions and shuffling operations to increase the frequency of memory accesses. GhostNet proposes a small model architecture, the Ghost module, which is designed to utilise fewer parameters and less computational power. The Ghost module initially generates a feature map via simple convolution and uses various inexpensive linear operations to expand the features and process the spatial information. A significant advantage of GhostNet is that it exhibits excellent feature extraction capability while simultaneously reducing the number of parameters [56]. Although there are no other studies in the literature for LPG cylinder surface defects, similar studies on defect detection with model lightweighting based on YOLO have come to the forefront, with some improvements in each of them. An improved YOLO-RDP model for metal surface defect detection based on YOLOv7-tiny has been proposed for application in edge devices [60]. In the Backbone part of the model, GSConv and VOV-GSCSP modules are used to optimise RexNet and the neck layer of the network. In addition, a dual-head object detection head called DdyHead is designed, and a pruning method is used. The experimental results showed that 3.7% and 3.5% improvement in mAP values were realised in the NEU-DET and GC10-DET datasets, respectively. In another study, a YOLOv5-based algorithm with three basic (GhostConv, ECAc3, and Decoupled Head) improvements was proposed for real-time detection of defects on the cover surface. The proposed model is named ESD-YOLOv5 [11]. The detection results of the ESD-YOLOv5 model on the study-specific dataset showed a 2.3% improvement in the mAP@50 metric compared to the YOLOv5 baseline model, but a 33% decrease in extraction speed from 137 FPS to 91 FPS. [PERSON] et al. [61] proposed a new defect detection model (SKS-YOLO) based on YOLOv5s with a simplified kernel and squeeze-excitation module for defect detection on steel plates. The SKS-YOLO model provided 4.4% improvement in accuracy compared to YOLOv5s. While the FPS value of YOLOv5s was 66, it decreased to 55 in SKS-YOLO, resulting in an 18% reduction in extraction speed. [PERSON] et al.