Open Access

ARTICLE

A Method for Small Target Detection and Counting of the End of Drill Pipes Based on the Improved YOLO11n

Miao Li^1,2,*, Xiaojun Li^1,3, Mingyang Zhao^1,2

1 School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo, 454000, China
2 School of Innovation and Entrepreneurship, Henan Polytechnic University, Jiaozuo, 454000, China
3 China Henan International Joint Laboratory of Coalmine Ground Control, Jiaozuo, 454000, China

* Corresponding Author: Miao Li. Email:

Computers, Materials & Continua 2025, 85(1), 1917-1936. https://doi.org/10.32604/cmc.2025.067382

Received 01 May 2025; Accepted 21 July 2025; Issue published 29 August 2025

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.

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