A Method for Small Target Detection and Counting of the End of Drill Pipes Based on the Improved YOLO11n
Source abstract
Abstract
Aiming at problems such as large errors and low efficiency in manual counting of drill pipes during drilling depth measurement, an intelligent detection and counting method for the small targets at the end of drill pipes based on the improved YOLO11n is proposed. This method realizes the high-precision detection of targets at drill pipe ends in the image by optimizing the target detection model, and combines a post-processing correction mechanism to improve the drill pipe counting accuracy. In order to alleviate the low-precision problem of YOLO11n algorithm for small target recognition in the complex underground background, the YOLO11n algorithm is improved. First, the key module C3k2 in the backbone network was improved, and Poly Kernel Inception (PKI) Block was introduced to replace Bottleneck in it to fully integrate the target context information and the model’s capability of feature extraction; Second, within the model’s neck network, a new feature fusion pyramid ISOP (Improved Small Object Pyramid) is proposed, SPDConv is introduced to strengthen the P2 feature, and CSP and OmniKernel are combined to integrate multi-scale features; Finally, the default loss function is substituted with Powerful-IoU (PIoU) to solve the anchor box expansion problem. On the self-built dataset, experimental verification was conducted. The findings showed that the Recall rose by 6.4%, mAP@0.5 increased by 4.5%, and mAP@0.5:0.95 improved by 6% compared with the baseline model, effectively solving the issues of false detection and missed detection problems in small target detection task. Meanwhile, we conducted counting tests on drilling videos from 5 different scenarios, achieving an average accuracy of 97.3%, which meets the accuracy needs for drill pipe recognition and counting in coal mine drilling sites. The research findings offer theoretical basis and technical backing for promoting the intelligent development of coal mine gas extraction drilling sites.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Measurement and Detection Methods · Optical Systems and Laser Technology · Advanced Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (43)
Show source-provided references
1. Coal mine gas migration model establishment and gas extraction technology field application research10.1016/j.fuel.2023.128650 ↗
2. Enhancing coal-gangue object detection using GAN-based data augmentation strategy with dual attention mechanism10.1016/j.energy.2023.129654 ↗
3. Research and practice of intelligent coal mine technology systems in China10.1007/s40789-022-00491-3 ↗
4. Slim-YOLO-PR_KD: an efficient pose-varied object detection method for underground coal mine10.1007/s11554-024-01539-0 ↗
5. Vision-based obstacle detection in dangerous region of coal mine driverless rail electric locomotives10.1016/j.measurement.2024.115514 ↗
6. Damage characteristics and YOLO automated crack detection of fissured rock masses under true-triaxial mining unloading conditions10.1016/j.engfracmech.2024.110790 ↗
7. Integrating real-time pose estimation and PPE detection with cutting-edge deep learning for enhanced safety and rescue operations in the mining industry10.1016/j.neucom.2024.129080 ↗
8. You only look once: unified, real-time object detection
9. YOLO9000: better, faster, stronger
10. Redmon J, Farhadi A. Yolov3: an incremental improvement. arXiv:1804.02767. 2018.
11. Research on mine-personnel helmet detection based on multi-strategy-improved YOLOv1110.3390/s25010170 ↗
12. Real-time detection of coal mine safety helmet based on improved YOLOv810.1007/s11554-024-01604-8 ↗
13. Coal mine conveyor belt foreign object detection based on feature enhancement and transformer
14. GDPs-YOLO: an improved YOLOv8s for coal gangue detection10.1080/19392699.2024.2346626 ↗
15. Research on mine personnel target detection method based on improved YOLOv810.1016/j.measurement.2024.116624 ↗
16. Rep-YOLO: an efficient detection method for mine personnel10.1007/s11554-023-01407-3 ↗
17. Research on real-time perception method of key targets in the comprehensive excavation working face of coal mine10.1088/1361-6501/ad060e ↗
18. Coal-rock interface real-time recognition based on the improved YOLO detection and bilateral segmentation network10.1016/j.undsp.2024.07.003 ↗
19. Localization and mapping method of unmanned mine trucks on underground inclined slopes based on laser SLAM
20. A survey and performance evaluation of deep learning methods for small object detection10.1016/j.eswa.2021.114602 ↗
21. Small object detection based on two-stage calculation transformer
22. DKTNet: dual-key transformer network for small object detection10.1016/j.neucom.2023.01.055 ↗
23. FFCA-YOLO for small object detection in remote sensing images10.1109/TGRS.2024.3510781 ↗
24. HIC-YOLOv5: improved YOLOv5 for small object detection
25. Intelligent monitoring of small target detection using YOLOv810.1016/j.aej.2024.10.114 ↗
26. DsP-YOLO: an anchor-free network with DsPAN for small object detection of multiscale defects10.1016/j.eswa.2023.122669 ↗
27. Research on underground drill pipe counting method based on improved ResNet network
28. Improved YOLOv8 method for counting drill rods in low-light images in coal mines
29. Drill pipe detection and counting based on improved YOLOv11 and Savitzky-Golay10.1038/s41598-025-01776-8 ↗
30. Research on automatic counting of drill pipes for underground gas drainage in coal mines based on YOLOv7-GFCA model10.3390/app131810240 ↗
31. A coal mine underground drill pipes counting method based on improved YOLOv8n
32. Drill pipe counting method based on improved spatial-temporal graph convolution neural network
33. Poly kernel inception network for remote sensing detection
34. StarCAN-PFD: an efficient and simplified multi-scale feature detection network for small objects in complex scenarios10.3390/electronics13153076 ↗
35. No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects
36. Omni-kernel network for image restoration
37. CSPNet: a new backbone that can enhance learning capability of CNN
38. Distance-IoU loss: faster and better learning for bounding box regression
39. Powerful-IoU: more straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism10.1016/j.neunet.2023.11.041 ↗
40. Yolov9: learning what you want to learn using programmable gradient information
41. Yolov10: real-time end-to-end object detection
42. Tian Y, Ye Q, Doermann D. YOLOv12: attention-centric real-time object detectors. arXiv:2502.12524. 2025. doi:10.48550/arxiv.2502.12524.
43. Mamba YOLO: a simple baseline for object detection with state space model