## 1 Introduction  
In the production process for broiler chickens, in order to reduce production costs enterprises have continuously improved the breeding technology for broilers, and their production performance has also improved. However, the rapid growth of the production of broilers has caused some problems, such as a significant increase in the incidence of leg diseases. According to statistics, approximately 14-30% of broiler chickens worldwide have leg diseases [1, 2]. In many cases, due to the lack of timely attention, broiler leg problems have caused large-scale losses for companies. A key to detecting leg diseases in broilers is the measurement of the length of the tibia. The traditional measurement method requires the dissection of a broiler leg and the measurement of the length of the tibia with a vernier caliper or tape measure. This is very inefficient and detrimental to the leg health of the broiler. In summary, in the current broiler chicken breeding industry, an efficient and accurate system for measuring a broiler's tibia is needed. In order to minimize the damage to broiler chickens after they pass inspections and to avoid the damage caused by traditional measurement methods, we introduced a fast digital X-ray photography system for photographing broiler chickens and accurately and efficiently measuring their tibia. Digital radiography (DR) is a digitized imaging technique in the realm of medicine [3], and it represents a digital evolution of traditional radiology. Presently, the applications of DR extend beyond human medical fields to include animal healthcare [4] as well as industrial inspection [5], among other domains. When applied to the task of leg disorder detection in broiler chickens, it facilitates the rapid imaging of these birds. The generated DR images exhibit a remarkable ability to distinguish bone structures from other tissues, thus aiding in subsequent length measurement procedures. To minimize the operational costs for enterprises, all DR images employed in this study were sourced exclusively from industrial computed tomography (CT) equipment. In order to solve the problem of measuring the length of the legs of broiler chickens, we designed a system for non-destructive measurement. The system consisted of two parts. The first part used industrial CT equipment to image the broiler chickens. The imaging process was very short and did not cause damage to the broiler chickens. The second part was the automatic determination of the leg tibia data contained in the broiler DR images based on deep learning. Using our improved Tibia-YOLO network, the tibia of a broiler's leg could be detected efficiently and accurately. After the network extracted the detection bounding box of the tibia, it extracted the pixel length of the long side of that box. Support vector regression (SVR) was used to predict broiler chickens' tibia lengths by using the pixel length. In the next part, we will discuss the difficulties encountered and solutions found during the process of improving the Tibia-YOLO network. As the field of deep learning has advanced, convolutional neural networks (CNNs) have come into focus, with a growing number of researchers leveraging CNNs on publicly available datasets for object detection tasks and yielding commendable detection results [6]. Concurrently, the utilization of digital radiography (DR) images has significantly enhanced the efficiency of medical practitioners. For instance, in the realms of pulmonary disease screening [7] and fracture detection [8], deep-learning techniques have assisted healthcare professionals in promptly assessing potential maladies. Most models adopted in deep learning have a deeper depth, but they produce problems such as excessive sequential processing and more latency [9]. We chose the You Only Look Once (YOLO) series as a baseline because it is a single-stage target detection method that can process a network more efficiently.