SEII-YOLO: a lightweight enhanced object detection model for substation equipment infrared images with applications in IoT embedded devices

XLiteYOLOv12: a lightweight framework for real-time hazardous object detection

The YOLOv12 network is adopted as the baseline model and the ADown module is introduced to improve downsampling efficiency while maintaining lightweight performance, and the BN-CGLU is incorporated into the A2C2f module to enhance the model’s nonlinear representation capability.

Industrial multi-object detection for automotive battery manufacturing using a CBAM-enhanced YOLOv5 framework

The mixing stage of automotive battery production requires reliable monitoring of raw material types, personnel actions, and correct tool use. However, accurate multi-scale object detection in complex scenes remains challenging because of background interference and the requirements of embedded deployment and real-time operation. This study proposes an enhanced YOLOv5 framework for industrial multi-object detection. The method adopts a dual-stage feature-enhancement strategy designed to improve robustness while limiting parameter count and computational complexity. First, the Convolutional Block Attention Module (CBAM) is embedded in the Backbone and Neck of YOLOv5 to provide multi-granularity feature enhancement and improve the detection of small objects, such as tools. Second, the conventional CIOU loss is replaced with the Focal-EIOU loss function to optimize bounding-box regression, reduce false detections across multiple target scales, and accelerate model convergence. Finally, K-means clustering is applied to target geometric features to generate specialized anchor-box parameters better suited to industrial scenarios. Experimental results on an automotive battery production-site dataset show that the improved model's mAP@0.5 increased by 2.4 % compared with the original model, reaching 95.2 %, while mAP@0.5:0.95 improved by 3.6 %, reaching 78.5 %. The proposed framework provides a lightweight and reliable solution for multi-object detection in complex industrial environments and supports the development of intelligent visual monitoring systems for smart manufacturing.

SODA-Net: A Lightweight Small Object Detection Network for Drone-Based Optical Sensor Systems

The widespread deployment of drone-based optical sensor systems in environmental monitoring, traffic management, and security surveillance has made small object detection a critical task in aerial sensing. However, the sensor-captured objects are typically small, low-resolution, and frequently occluded, posing challenges for achieving high accuracy under resource-constrained conditions. To address these challenges, this article proposes SODA-Net, a lightweight small object detection network tailored for drone optical sensor imagery, enabling efficient real-time inference on edge devices. The proposed method integrates four key modules to enhance feature representation with low computational cost. A shallow feature enhancement (SFE) module improves early feature extraction and semantic fusion. A detail enhancement and feature fusion (DEFF) module strengthens edge and texture representation using lightweight reparameterized convolutions. An attention-guided lightweight downsampling (ALDS) module preserves sensitivity to small objects while improving feature utilization. In addition, a ghost shuffle-based spatial pyramid pooling-fast module enlarges the receptive field for global context aggregation and multiscale fusion. Experiments on the VisDrone2019 dataset show that SODA-Net improves mAP50 by 4.4% while reducing parameters by 77.3%. Further validation on TinyPerson and aerial image tiny object detection (AITOD) demonstrates strong generalization, with mAP50 gains of 5.0% and 6.8%, respectively. The results indicate its effectiveness for real-time deployment in resource-limited aerial optical sensor systems.

SOEM-DETR: an efficient real-time drone infrared image photovoltaic defect detection model

Improving Image Object Recognition Accuracy in V2X Environments Using the CBAM-MobileNet Model

Addressing the issue of low object recognition accuracy in real-time intelligent vehicle-to-everything (V2X) environments, where sparse information and severe occlusion of small objects often degrade perception performance, this paper proposes an engineered lightweight detection framework based on YOLOv8s. Reliable object perception in V2X systems is essential for intelligent wireless communication networks and electromagnetic sensing environments, where accurate interpretation of visual information supports cooperative perception and real-time decision-making. The proposed framework replaces the original backbone with a lightweight MobileNetV3-CBAM model, employing depthwise separable convolutions and the CBAM attention mechanism to improve computational efficiency while preserving fine-grained features. The neck adopts weighted bidirectional feature fusion to effectively integrate shallow high-resolution information with deep semantic representations through learnable upsampling and downsampling weights. Furthermore, the detection head is optimized using SIOU loss to enhance localization sensitivity and focal loss to alleviate category imbalance. Experiments conducted on the DAIR-V2X-C dataset demonstrate that the proposed method achieves an mAP@0.5 of 95.5% and an mAP @[0.5:0.95] of 73.4%. For small objects, the mAP@0.5 reaches 88.1%, while under an extreme occlusion level of 0.2, the model still maintains an mAP@0.5 of 80.4%. Meanwhile, the average inference latency remains as low as 13.5 ms, demonstrating its suitability for real-time edge deployment. The proposed framework provides an efficient solution for lightweight perception in V2X systems and offers valuable technical references for intelligent electromagnetic sensing and wireless propagation-aware visual perception applications.

AN ATTENTION-INTEGRATED YOLO11-S MODEL FOR OBJECT DETECTION IN SATELLITE IMAGERY: PERFORMANCE ANALYSIS AND COMPARISON

As remote sensing technologies improve, we are now able to look at the Earth from different points of view. These technologies have enabled major changes in many areas. High-resolution satellite images have enabled progress in civilian and military applications such as environmental monitoring, disaster management, and public safety. While these images give us much information, we also need advanced analysis and automated object detection. Recent studies employing deep learning architectures reached high success rates in object detection from satellite imagery. This study introduces a novel model by assessing the impacts of several YOLO object detection algorithms with the Convolutional Block Attention Module (CBAM) on aircraft detection from satellite images. We used the HRPlanesv2 dataset for our experiments. The results revealed that the proposed model had better performance compared to alternative models. The proposed model achieved a mAP50 of 0.9868, a mAP50-95 of 0.7911, a precision of 0.9811, and a recall of 0.9637. Incorporating CBAM improves the detection of objects in crowded and complex scenes. These results demonstrate that attention mechanisms have a significant impact when used with the YOLO architecture for object detection in satellite images. It also provides a reliable and efficient solution for practical applications requiring accurate and consistent aircraft detection.