Research on non-tobacco related materials recognition method based on YOLOv8

TL;DR

This paper presents an improved YOLOv8 model, NTRM-YOLO, for detecting non-tobacco materials in industrial processing, achieving better accuracy and efficiency.

Contribution

The novel NTRM-YOLO model integrates attention mechanisms and GhostConv to enhance detection accuracy while reducing model parameters.

Findings

NTRM-YOLO achieved 95.6% detection performance, a 2% improvement over the baseline model.

The model reduced parameters to 10.0 MB, a 10% decrease compared to the baseline.

Experiments validated the model's effectiveness for industrial non-tobacco material detection.

Abstract

Enhancing non-tobacco related materials control and improving the purity of tobacco leaves have emerged as pivotal quality indicators for raw material processing in both domestic and foreign industrial enterprises. In order to accurately detect non-tobacco related materials, this paper introduces an enhanced variant of the YOLOv8(You Only Look Once version 8) model, termed NTRM-YOLO. NTRM-YOLO use deep learning methods to detect non-tobacco related materials. The attention mechanism module is integrated into the backbone network of NTRM-YOLO, aimed at enhancing the delineation of non-tobacco related materials features, thereby bolstering the detection efficacy of the model. In order to reduce the number of model parameters, this paper integrates GhostConv(Ghost Convolution) module within the neck network, alongside the design integration of a GhostConv-C2F module. This strategic substitution serves to diminish the model’s parameters while concurrently enhancing its capacity for feature expression. Within the Head network, capitalizing fully on the merits of multiple attention mechanisms, Dyhead(Dynamic Head) is introduced with the aim of markedly enhancing the detection accuracy of the model. This study also optimized the loss function by using the vector angle. Moreover, this paper uses industrial camera sensors to collect images containing non-tobacco related materials and constructed of an NTRM dataset after preprocessing. Subsequently, a meticulously series of experiments was conducted on the NTRM dataset to showcase the efficacy of NTRM-YOLO model in applications pertaining to non-tobacco related materials detection. The experimental findings reveal that in contrast to the baseline model, NTRM-YOLO attained a detection performance of 95.6%, marking a notable improvement of 2% over the baseline model. Additionally, it exhibited a parameters of 10.0 MB, reflecting a 10% reduction compared to the baseline model. These experiments furnishes a theoretical foundation and technical substantiation for the subsequent advancement of more refined industrial impurity removal instruments and equipment.

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