Object Detection-Based System for Traffic Signs on Drone-Captured Images

Naranjo, Manuel; Fuentes, Diego; Muelas, Elena; Díez, Enrique; Ciruelo, Luis; Alonso, César L.; Eduardo, Abenza,; Gómez-Espinosa, Roberto; Luengo, Inmaculada

doi:10.3390/drones7020112

Cited by 14 publications

(2 citation statements)

References 40 publications

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“…Mean average precision (mAP) measures in what percentage the algorithm predicted the object from all individual classes correctly [50]. mAP is calculated using the averaged AP of all classes [59]. Equation (4) presents the mathematical description of mAP, where AP k is the average precision of the classes and n is the number of classes [60].…”

Section: Evaluation Indexes For the Deep Learning Modelsmentioning

confidence: 99%

An Assessment of Human Inspection and Deep Learning for Defect Identification in Floral Wreaths

Caballero-Ramirez,

Baez-Lopez,

Limon-Romero

et al. 2023

Horticulturae

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Quality assurance through visual inspection plays a pivotal role in agriculture. In recent years, deep learning techniques (DL) have demonstrated promising results in object recognition. Despite this progress, few studies have focused on assessing human visual inspection and DL for defect identification. This study aims to evaluate visual human inspection and the suitability of using DL for defect identification in products of the floriculture industry. We used a sample of defective and correct decorative wreaths to conduct an attribute agreement analysis between inspectors and quality standards. Additionally, we computed the precision, accuracy, and Kappa statistics. For the DL approach, a dataset of wreath images was curated for training and testing the performance of YOLOv4-tiny, YOLOv5, YOLOv8, and ResNet50 models for defect identification. When assessing five classes, inspectors showed an overall precision of 92.4% and an accuracy of 97%, just below the precision of 93.8% obtained using YOLOv8 and YOLOv5 with accuracies of 99.9% and 99.8%, respectively. With a Kappa value of 0.941, our findings reveal an adequate agreement between inspectors and the standard. The results evidence that the models presented a similar performance to humans in terms of precision and accuracy, highlighting the suitability of DL in assisting humans with defect identification in artisanal-made products from floriculture. Therefore, by assisting humans with digital technologies, organizations can embrace the full potential of Industry 4.0, making the inspection process more intelligent and reliable.

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Section: Evaluation Indexes For the Deep Learning Modelsmentioning

confidence: 99%

An Assessment of Human Inspection and Deep Learning for Defect Identification in Floral Wreaths

Caballero-Ramirez,

Baez-Lopez,

Limon-Romero

et al. 2023

Horticulturae

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“…The increasing prevalence of UAV aerial images across diverse applications has provided a fertile ground for the integration of target detection and aerial image fusion. This integration has proven particularly advantageous in fields such as urban transportation [1], urban planning [2], and environmental monitoring [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Small Object Detection in Aerial Images: A Novel Approach with PCSG Model

An,

Duanmu,

et al. 2024

Aerospace

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Generalized target detection algorithms perform well for large- and medium-sized targets but struggle with small ones. However, with the growing importance of aerial images in urban transportation and environmental monitoring, detecting small targets in such imagery has been a promising research hotspot. The challenge in small object detection lies in the limited pixel proportion and the complexity of feature extraction. Moreover, current mainstream detection algorithms tend to be overly complex, leading to structural redundancy for small objects. To cope with these challenges, this paper recommends the PCSG model based on yolov5, which optimizes both the detection head and backbone networks. (1) An enhanced detection header is introduced, featuring a new structure that enhances the feature pyramid network and the path aggregation network. This enhancement bolsters the model’s shallow feature reuse capability and introduces a dedicated detection layer for smaller objects. Additionally, redundant structures in the network are pruned, and the lightweight and versatile upsampling operator CARAFE is used to optimize the upsampling algorithm. (2) The paper proposes the module named SPD-Conv to replace the strided convolution operation and pooling structures in yolov5, thereby enhancing the backbone’s feature extraction capability. Furthermore, Ghost convolution is utilized to optimize the parameter count, ensuring that the backbone meets the real-time needs of aerial image detection. The experimental results from the RSOD dataset show that the PCSG model exhibits superior detection performance. The value of mAP increases from 97.1% to 97.8%, while the number of model parameters decreases by 22.3%, from 1,761,871 to 1,368,823. These findings unequivocally highlight the effectiveness of this approach.

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