MSIA-Net: A Lightweight Infrared Target Detection Network with Efficient Information Fusion

Yu, Jun; Li, Shun; Zhou, Shangbo; Wang, Hui

doi:10.3390/e25050808

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…By embedding location information into channel attention, the CA attention mechanism enables the model to obtain information about a larger area. The CA attention mechanism contains both channel and spatial attention modules, outperforming SE with only the channel mechanism [30,31]. The structure diagram of the CA attention mechanism is shown in Figure 5.…”

Section: Optimization Of the Backbone Networkmentioning

confidence: 99%

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

Zhang

Xuan

Jing

et al. 2023

Animals

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The accurate identification of sheep is crucial for breeding, behavioral research, food quality tracking, and disease prevention on modern farms. As a result of the time-consuming, expensive, and unreliable problems of traditional sheep-identification methods, relevant studies have built sheep face recognition models to recognize sheep through facial images. However, the existing sheep face recognition models face problems such as high computational costs, large model sizes, and weak practicality. In response to the above issues, this study proposes a lightweight sheep face recognition model named LSR-YOLO. Specifically, the ShuffleNetv2 module and Ghost module were used to replace the feature extraction module in the backbone and neck of YOLOv5s to reduce floating-point operations per second (FLOPs) and parameters. In addition, the coordinated attention (CA) module was introduced into the backbone to suppress non-critical information and improve the feature extraction ability of the recognition model. We collected facial images of 63 small-tailed Han sheep to construct a sheep face dataset and further evaluate the proposed method. Compared to YOLOv5s, the FLOPs and parameters of LSR-YOLO decreased by 25.5% and 33.4%, respectively. LSR-YOLO achieved the best performance on the sheep face dataset, and the mAP@0.5 reached 97.8% when the model size was only 9.5 MB. The experimental results show that LSR-YOLO has significant advantages in recognition accuracy and model size. Finally, we integrated LSR-YOLO into mobile devices and further developed a recognition system to achieve real-time recognition. The results show that LSR-YOLO is an effective method for identifying sheep. The method has high recognition accuracy and fast recognition speed, which gives it a high application value in mobile recognition and welfare breeding.

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Section: Optimization Of the Backbone Networkmentioning

confidence: 99%

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

Zhang

Xuan

Jing

et al. 2023

Animals

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“…This leads to a sharp decline in performance. To tackle this puzzle, infrared detection technology has emerged, finding widespread use in diverse domains like cellular tumor diagnosis [2], privacy preservation [3], marine rescue [4,5], UAV navigation [6,7], and autonomous driving [8,9]. As a result, detecting infrared objects in real time with precision within low-contrast conditions has become a challenge to solve.…”

Section: Introductionmentioning

confidence: 99%

Infrared Target Detection Based on Interval Sampling Weighting and 3D Attention Head in Complex Scenario

Yu,

Wang,

Zhou

et al. 2023

Applied Sciences

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Thermal infrared detection technology can enable night vision and is robust in complex environments, making it highly advantageous for various fields. However, infrared images have low resolution and high noise, resulting in limited detailed information being available about the target object. This difficulty is further amplified when detecting small targets, which are prone to occlusion. In response to these challenges, we propose a model for infrared target detection designed to achieve efficient feature representation. Firstly, an interval sampling weighted (ISW) module is proposed, which strengthens the fusion network’s spatial relationship modeling, thereby elevating the model’s generalization capability across diverse target-density regions. Next, a detection head founded on 3D attention (TAHNet) is introduced, which helps the network more comprehensively understand the feature details of the target. This enhances the accuracy of the model in identifying the target object’s location, reduces false positives and false negatives, and optimizes the network’s performance. Furthermore, to our model, we introduce the C2f module to transfer gradient information across multiple branches. The features learned using diverse branches interact and fuse in subsequent stages, further enhancing the model’s representation ability and understanding of the target. Experimental outcomes validate the efficacy of the proposed model, showcasing state-of-the-art detection performance on FLIR and KAIST thermal infrared datasets and showing strong antiocclusion and robustness in complex scenes.

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Improved lightweight infrared road target detection method based on YOLOv8

Yao,

Xu,

Feijiang

et al. 2024

Infrared Physics & Technology

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MSIA-Net: A Lightweight Infrared Target Detection Network with Efficient Information Fusion

Cited by 6 publications

References 30 publications

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

Infrared Target Detection Based on Interval Sampling Weighting and 3D Attention Head in Complex Scenario

Improved lightweight infrared road target detection method based on YOLOv8

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