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Object Recognition with Vision Sensors Based on Machine Learning and Deep Learning

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (5 March 2025) | Viewed by 16423

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Special Issue Editors

Dr. Meng Yang

E-Mail Website
Guest Editor

School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China
Interests: image understanding; machine learning; language understanding; open-world semantic understanding

Dr. Jianjun Qian

E-Mail Website
Guest Editor

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: pattern recognition; computer vision

Special Issue Information

Dear Colleagues,

As massive vision sensors have been established, object recognition has achieved significant success due to visual big data and advanced machine learning methods (e.g., deep learning). However, there are still several challenges, e.g., the assumption of closed-world recognition, the requirement of large-scale labeled images, the problem of few-shot learning, and poor model interpretability. This Special Issue aims to address the object recognition methods with vision sensors designed to meet these challenges.

Dr. Meng Yang
Dr. Jianjun Qian
Guest Editors

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Keywords

open-world object recognition
semi-supervised object recognition
weakly supervised object recognition
unsupervised object recognition
few-shot object recognition
interpretable object recognition
object recognition based on deep learning
object recognition based on machine learning

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Published Papers (6 papers)

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Research

18 pages, 4465 KiB

Open AccessArticle

HYFF-CB: Hybrid Feature Fusion Visual Model for Cargo Boxes

by Juedong Li, Kaifan Yang, Cheng Qiu, Lubin Wang, Yujia Cai, Hailan Wei, Qiang Yu and Peng Huang

Sensors 2025, 25(6), 1865; https://doi.org/10.3390/s25061865 - 17 Mar 2025

Viewed by 243

Abstract

In automatic loading and unloading systems, it is crucial to accurately detect the locations of boxes inside trucks in real time. However, the existing methods for box detection have multiple shortcomings, and can hardly meet the strict requirements of actual production. When the truck environment is complex, the currently common models based on convolutional neural networks show certain limitations in the practical application of box detection. For example, these models fail to effectively handle the size inconsistency and occlusion of boxes, resulting in a decrease in detection accuracy. These problems seriously restrict the performance and reliability of automatic loading and unloading systems, making it impossible to achieve ideal detection accuracy, speed, and adaptability. Therefore, there is an urgent need for a new and more effective box detection method. To this end, this paper proposes a new model, HYFF-CB, which incorporates key technologies such as a location attention mechanism, a fusion-enhanced pyramid structure, and a synergistic weighted loss system. After real-time images of a truck were obtained by an industrial camera, the HYFF-CB model was used to detect the boxes in the truck, having the capability to accurately detect the stacking locations and quantity of the boxes. After rigorous testing, the HYFF-CB model was compared with other existing models. The results show that the HYFF-CB model has apparent advantages in detection rate. With its detection performance and effect fully meeting the actual application requirements of automatic loading and unloading systems, the HYFF-CB model can excellently adapt to various complex and changing scenarios for the application of automatic loading and unloading. Full article

(This article belongs to the Special Issue Object Recognition with Vision Sensors Based on Machine Learning and Deep Learning)

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23 pages, 6672 KiB

Open AccessArticle

A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage

by Jonathan Gregory, Scott M. Miehls, Jesse L. Eickholt and Daniel P. Zielinski

Sensors 2025, 25(4), 1022; https://doi.org/10.3390/s25041022 - 9 Feb 2025

Cited by 1 | Viewed by 1145

Abstract

Recent advances in fish transportation technologies and deep machine learning-based fish classification have created an opportunity for real-time, autonomous fish sorting through a selective passage mechanism. This research presents a case study of a novel application that utilizes deep machine learning to detect partially dewatered fish exiting an Archimedes Screw Fish Lift (ASFL). A MobileNet SSD model was trained on images of partially dewatered fish volitionally passing through an ASFL. Then, this model was integrated with a network video recorder to monitor video from the ASFL. Additional models were also trained using images from a similar fish scanning device to test the feasibility of this approach for fish classification. Open source software and edge computing design principles were employed to ensure that the system is capable of fast data processing. The findings from this research demonstrate that such a system integrated with an ASFL can support real-time fish detection. This research contributes to the goal of automated data collection in a selective fish passage system and presents a viable path towards realizing optical fish sorting. Full article

(This article belongs to the Special Issue Object Recognition with Vision Sensors Based on Machine Learning and Deep Learning)

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19 pages, 11309 KiB

Open AccessArticle

FSH-DETR: An Efficient End-to-End Fire Smoke and Human Detection Based on a Deformable DEtection TRansformer (DETR)

by Tianyu Liang and Guigen Zeng

Sensors 2024, 24(13), 4077; https://doi.org/10.3390/s24134077 - 23 Jun 2024

Cited by 7 | Viewed by 1937

Abstract

Fire is a significant security threat that can lead to casualties, property damage, and environmental damage. Despite the availability of object-detection algorithms, challenges persist in detecting fires, smoke, and humans. These challenges include poor performance in detecting small fires and smoke, as well as a high computational cost, which limits deployments. In this paper, we propose an end-to-end object detector for fire, smoke, and human detection based on Deformable DETR (DEtection TRansformer) called FSH-DETR. To effectively process multi-scale fire and smoke features, we propose a novel Mixed Encoder, which integrates SSFI (Separate Single-scale Feature Interaction Module) and CCFM (CNN-based Cross-scale Feature Fusion Module) for multi-scale fire, smoke, and human feature fusion. Furthermore, we enhance the convergence speed of FSH-DETR by incorporating a bounding box loss function called PIoUv2 (Powerful Intersection of Union), which improves the precision of fire, smoke, and human detection. Extensive experiments on the public dataset demonstrate that the proposed method surpasses state-of-the-art methods in terms of the

m A P

(mean Average Precision), with

m A P

and

{m A P}_{50}

reaching 66.7% and 84.2%, respectively. Full article

(This article belongs to the Special Issue Object Recognition with Vision Sensors Based on Machine Learning and Deep Learning)

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15 pages, 529 KiB

Open AccessArticle

A Pix2Pix Architecture for Complete Offline Handwritten Text Normalization

by Alvaro Barreiro-Garrido, Victoria Ruiz-Parrado, A. Belen Moreno and Jose F. Velez

Sensors 2024, 24(12), 3892; https://doi.org/10.3390/s24123892 - 16 Jun 2024

Cited by 2 | Viewed by 1701

Abstract

In the realm of offline handwritten text recognition, numerous normalization algorithms have been developed over the years to serve as preprocessing steps prior to applying automatic recognition models to handwritten text scanned images. These algorithms have demonstrated effectiveness in enhancing the overall performance of recognition architectures. However, many of these methods rely heavily on heuristic strategies that are not seamlessly integrated with the recognition architecture itself. This paper introduces the use of a Pix2Pix trainable model, a specific type of conditional generative adversarial network, as the method to normalize handwritten text images. Also, this algorithm can be seamlessly integrated as the initial stage of any deep learning architecture designed for handwritten recognition tasks. All of this facilitates training the normalization and recognition components as a unified whole, while still maintaining some interpretability of each module. Our proposed normalization approach learns from a blend of heuristic transformations applied to text images, aiming to mitigate the impact of intra-personal handwriting variability among different writers. As a result, it achieves slope and slant normalizations, alongside other conventional preprocessing objectives, such as normalizing the size of text ascenders and descenders. We will demonstrate that the proposed architecture replicates, and in certain cases surpasses, the results of a widely used heuristic algorithm across two metrics and when integrated as the first step of a deep recognition architecture. Full article

(This article belongs to the Special Issue Object Recognition with Vision Sensors Based on Machine Learning and Deep Learning)

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15 pages, 9386 KiB

Open AccessArticle

Three-Dimensional Positioning for Aircraft Using IoT Devices Equipped with a Fish-Eye Camera

by Junichi Mori, Makoto Morinaga, Takumi Asakura, Takenobu Tsuchiya, Ippei Yamamoto, Kentaro Nishino and Shigenori Yokoshima

Sensors 2023, 23(22), 9108; https://doi.org/10.3390/s23229108 - 10 Nov 2023

Viewed by 1597

Abstract

Radar is an important sensing technology for three-dimensional positioning of aircraft. This method requires detecting the response from the object to the signal transmitted from the antenna, but the accuracy becomes unstable due to effects such as obstruction and reflection from surrounding buildings at low altitudes near the antenna. Accordingly, there is a need for a ground-based positioning method with high accuracy. Among the positioning methods using cameras that have been proposed for this purpose, we have developed a multisite synchronized positioning system using IoT devices equipped with a fish-eye camera, and have been investigating its performance. This report describes the details and calibration experiments for this technology. Also, a case study was performed in which flight paths measured by existing GPS positioning were compared with results from the proposed method. Although the results obtained by each of the methods showed individual characteristics, the three-dimensional coordinates were a good match, showing the effectiveness of the positioning technology proposed in this study. Full article

(This article belongs to the Special Issue Object Recognition with Vision Sensors Based on Machine Learning and Deep Learning)

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24 pages, 3227 KiB

Open AccessArticle

An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images

by Saydirasulov Norkobil Saydirasulovich, Mukhriddin Mukhiddinov, Oybek Djuraev, Akmalbek Abdusalomov and Young-Im Cho

Sensors 2023, 23(20), 8374; https://doi.org/10.3390/s23208374 - 10 Oct 2023

Cited by 61 | Viewed by 8642

Abstract

Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources. This study presents an enhanced YOLOv8 model customized to the context of unmanned aerial vehicle (UAV) images to address the challenges above and attain heightened precision in detection accuracy. Firstly, the research incorporates Wise-IoU (WIoU) v3 as a regression loss for bounding boxes, supplemented by a reasonable gradient allocation strategy that prioritizes samples of common quality. This strategic approach enhances the model’s capacity for precise localization. Secondly, the conventional convolutional process within the intermediate neck layer is substituted with the Ghost Shuffle Convolution mechanism. This strategic substitution reduces model parameters and expedites the convergence rate. Thirdly, recognizing the challenge of inadequately capturing salient features of forest fire smoke within intricate wooded settings, this study introduces the BiFormer attention mechanism. This mechanism strategically directs the model’s attention towards the feature intricacies of forest fire smoke, simultaneously suppressing the influence of irrelevant, non-target background information. The obtained experimental findings highlight the enhanced YOLOv8 model’s effectiveness in smoke detection, proving an average precision (AP) of 79.4%, signifying a notable 3.3% enhancement over the baseline. The model’s performance extends to average precision small (APS) and average precision large (APL), registering robust values of 71.3% and 92.6%, respectively. Full article

(This article belongs to the Special Issue Object Recognition with Vision Sensors Based on Machine Learning and Deep Learning)

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