## 1 Introduction  
Unmanned drilling rigs are widely adopted because they integrate real-time monitoring systems that enable environmental awareness and dynamic risk alerts during drilling. In risk prediction, the motion characteristics of drill cuttings serve as key indicators of the drill bit's working condition, the bottom-hole flow field, and other drilling dynamics. Accurate detection and tracking of drill cuttings can thus provide valuable insights into borehole conditions, offering significant engineering benefits. With the rapid advancement of unmanned drilling technology over the past decade, numerous industrial methods for drill-cutting detection have emerged. Ref. [1] proposedan acoustic detection approach, analyzing the sound signals generated by drill cuttings impacting the pipe wall or moving through drilling fluids to infer their velocity and distribution. While suitable for downhole environments without requiring optical equipment, this method is highly susceptible to environmental noise and fluid property variations, limiting its measurement accuracy. With the increasing adoption of radar and laser technologies, some systems utilize Laser Doppler Velocimetry (LDV) to measure the velocity and particle size distribution of drill cuttings [2], while systems requiring high-precision real-time monitoring have employed millimeter-wave (MMW) radar for flow velocity and distribution measurements [3]. Although these methods offer high detection accuracy, they suffer from high costs and vulnerability to dust and electromagnetic noise under harsh conditions. Pressure sensors, due to their low cost and simple installation [4], are widely favored. By installing pressure sensors near the drill string or drill bit, the flow behavior of drill cuttings can be inferred from fluctuations in drilling fluid pressure. However, pressure-based methods cannot directly measure particle size or velocity, and downhole conditions such as high temperature, high pressure, and severe vibrations often lead to sensor zero drift or signal distortion, thereby compromising measurement stability. Leveraging computer vision, drill-cutting monitoring can achieve low-cost, highly stable, all-weather, non-contact, real-time perception by simply addressing the issue of insufficient downhole illumination. As numerous solutions to the illumination challenge already exist, this study focuses on two core technologies critical for drill-cutting velocity measurement: object detection [5] and multi-object tracking (MOT) [6]. Object detection is employed to identify drill cuttings and provide precise localization, while the MOT algorithm ensures consistent target association across frames, thereby supporting accurate velocity computation. The current object detection techniques can be broadly categorized into one-stage [7] and two-stage approaches [8]. The YOLO series [9], representing one-stage methods, excels in high real-time requirements due to its end-to-end training and rapid inference capabilities. In contrast, two-stage methods--such as the Region-Based Convolutional Neural Network (R-CNN), Fast R-CNN, and Faster R-CNN proposed by [10; 11; 12]--enhance detection accuracy by generating candidate regions followed by classification and regression. However, their high computational complexity limits their applicability in real-time industrial monitoring. Effective detection of flying drill cuttings requires the deployment of multi-object tracking (MOT) algorithms. Based on the independence of detection and tracking stages, MOT approaches can be divided into Detection-Based Tracking (DBT) [13] and Joint Detection and Tracking (JDT) [14]. JDT methods require manual initialization, involve high computational overhead, struggle to recover from target loss, and exhibit limited adaptability to the appearance variations in drill cuttings, rendering them unsuitable for the harsh operational environments of unmanned drilling rigs.