## 1 Introduction
Deep learning methods have already been successfully applied in various industries, including printed circuit boards [13], transmission line elements [14], steel sheets [15; 16], metallic components [17], textile surface defects [18], and wood surfaces [19]. It is especially deep learning methods involving ANNs that can achieve this automatic extraction of intricate patterns and features from data features that is so necessary for industrial defect detection. Among these methods, convolutional neural network (CNN) algorithms have achieved huge successes recently in image-based defect detection by making inferences at multiple layers through multiscale analysis. Such techniques can even catch defects that might be too small for the resolution power of the human naked eye. The CNN algorithms commonly used in defect detection may be broadly divided into two categories: two-stage methods that construct bounding boxes and estimate information regarding defect location and class and one-stage methods that estimate this information directly. Two-stage methods include RCNN [20], Fast-RCNN [21], and Faster-RCNN [22], while popular one-stage methods include the series of SSD [23] and YOLO [24]. Deep learning models with lightweight architectures are preferred for real-time and efficient execution of defect detection tasks with CNN methods on resource-constrained devices. Surface defect detection with lightweight deep learning models focuses on You Only Look Once (YOLO) architecture, which includes state-of-the-art (SOTA) networks. However, ongoing developments in green deep learning aim to further optimise models such as YOLO to reduce energy costs and carbon emissions while maintaining performance [25; 26]. Versions such as YOLOv4 [27] and YOLOv5 [28] have gained widespread adoption in real-time applications, offering fast processing times and high detection accuracy. Numerous strategies have been explored in existing studies to enhance the standard architecture of YOLOv5 to achieve the desired application-specific performance in demanding industrial applications, such as detecting surface defects on industrial liquid gas cylinders with complex backgrounds. These strategies include developing the efficient channel attention (ECA) mechanism for optimising channel-based attention mechanisms and improving the feature extraction capabilities of the networks [29; 30; 31]. Ghost convolution and other types of lightweight convolutional layers are used to reduce computational overhead and increase network speed [32; 33; 34]. This study proposes a YOLOv5-based network design aimed at detecting surface defects on LPG cylinders in resource-constrained devices. The basic model is customised to adapt to the characteristics of LPG cylinder surface defects, limited resources, and real-time detection requirements. The YOLOv5s network is designed to reduce model size, parameter count, computational cost, and carbon footprint. The study's contributions and innovations to the literature are listed below:  
* A new dataset of 2360 LPG cylinder images with defective surfaces is proposed. * To lighten the network, the Ghost module was used both in the backbone and neck sections of YOLOv5. In addition, the C3 Ghost module has been added to the neck section, and the P5 head, responsible for detecting large objects, has been removed from the head. In this way, low memory consumption with small model size, reduced computing costs, and fast inference are achieved. * In this network, ECA is a channel-based attention mechanism that further optimises this network by enhancing the feature extraction capability to recognise accurate surface defects. * The efficacy of the proposed model is validated through comprehensive experimentation on a custom dataset. The following part of the paper, Section 2, presents studies on defect detection, lightweight networks, and YOLO networks. A detailed explanation of the detection methodology developed in this study is provided in Section 3.