## 2 Related Work  
The field of object detection has undergone significant evolution, particularly with the development of deep learning techniques [11; 12; 13]. Initially, the focus was primarily on Convolutional Neural Networks (CNNs), which have been the backbone of many groundbreaking advancements in image analysis. CNNs, with their ability to extract and learn features from images, have been pivotal in tasks ranging from facial recognition to autonomous driving. However, recent trends have seen a shift towards Transformer-based models, which were originally developed for natural language processing tasks [14; 15; 16]. These models, unlike CNNs, are adept at handling sequential data and can capture long-range dependencies in data, making them particularly suitable for complex tasks like object detection. Transformers have shown remarkable capabilities in understanding the context and relationships within an image, leading to more accurate and efficient object detection systems. One of the key strengths of Transformer models is their scalability and ability to handle large datasets, which is crucial in fields like medical imaging and autonomous driving where large amounts of data are processed [17]. This transition from CNNs to Transformer-based models marks a significant shift in the landscape of object detection, offering new possibilities and efficiencies in various applications. In the realm of medical image analysis, deep learning has achieved remarkable results for various problems. For instance, [PERSON] and others proposed a deep learning method based on CNNs to enhance the detection of COVID-19 and its variants from chest X-ray images [18]. Furthermore, [PERSON] and colleagues developed a method for detecting breast calcifications using deep learning, capable of classifying the presence of calcifications in the breast [19]. Significant research has also been conducted in the field of object detection. [PERSON] and team [20] introduced a new method based on computer vision and object detection technologies, utilizing the Convolutional Neural Network EfficientDet-D2 model, for cloud detection in image sequences. [PERSON] and others [21] proposed an improved deep neural network model, based on YOLOv5, for real-time detection of road surface damage in photographic representations of outdoor road surfaces. Efforts have been made to improve object detection methods for medical image applications. For example, U-Net architecture was proposed to improve the adaptability to medical image features by introducing domain expert knowledge and specific data enhancement strategies [22]. A multi-level deep learning framework for lung nodule detection was proposed using multi-scale and multi-level features [23]. However, these approaches do not adequately address the unique challenges presented by orthopedic medical device detection. Additionally, some studies have combined deep learning with other types of features to enhance detection performance. For example, [PERSON] and team [24] presented a novel approach using CNNs for melanoma detection, incorporating manual texture features of dermatoscopic images as additional input during the training phase. Specific improvements in networks have also emerged in particular application domains. For instance, [PERSON] and others proposed a MobileNet V2 network with a dual attention mechanism, enabling spatial and channel dimension operations at the network level for plant disease identification [25]. [PERSON] and team introduced an innovative improvement of the VGG19 network (ParaNet+), used for classifying flicker images into normal and abnormal categories, applying the Grad-CAM++ algorithm to locate abnormal parathyroid glands [26]. However, despite these advancements, existing detection models often fail to meet the unique requirements of orthopedic medical devices. To address this gap, we propose OrthoDETR, a Transformer-based object detection model specifically designed and optimized for orthopedic medical equipment.