Training results of YOLOv7-tiny model and G-YOLO model on hazardous materials vehicle dataset.

(a) [email protected] Curves Comparison; (b) [email protected]: .95 Curves Comparison.</p

Ghost module.

Hazardous chemical vehicles are specialized vehicles used for transporting flammable gases, medical waste, and liquid chemicals, among other dangerous chemical substances. During their transportation, there are risks of fire, explosion, and leakage of hazardous materials, posing serious threats to human safety and the environment. To mitigate these possible hazards and decrease their probability, this study proposes a lightweight object detection method for hazardous chemical vehicles based on the YOLOv7-tiny model.The method first introduces a lightweight feature extraction structure, E-GhostV2 network, into the trunk and neck of the model to achieve effective feature extraction while reducing the burden of the model. Additionally, the PConv is used in the model’s backbone to effectively reduce redundant computations and memory access, thereby enhancing efficiency and feature extraction capabilities. Furthermore, to address the problem of performance degradation caused by overemphasizing high-quality samples, the model adopts the WIoU loss function, which balances the training effect of high-quality and low-quality samples, enhancing the model’s robustness and generalization performance. Experimental results demonstrate that the improved model achieves satisfactory detection accuracy while reducing the number of model parameters, providing robust support for theoretical research and practical applications in the field of hazardous chemical vehicle object detection.</div

Validation instances of YOLOv7-tiny model and G-YOLO model on cars detection dataset.

(a) illustrates the validation instances of the YOLOv7-tiny model; (b) illustrates the validation instances of the G-YOLO model.</p

High Interfacial Thermal Stability of Flexible Flake-Structured Aluminum Thin-Film Electrodes for Bi₂Te₃‑Based Thermoelectric Devices

Environmental thermal energy harvesting based on thermoelectric devices is greatly significant to the advancement of next-generation self-powered wearable electronic devices. However, the rigid electrodes and interface diffusion of electrodes/thermoelectric materials would lead to the wearable discomfort and performance degradation of the thermoelectric device. Here, a flake-structured Al thin-film electrode with high conductivity and excellent reliability is prepared by regulating the microstructure and crystallinity of the films. The as-prepared Al thin film not only maintains its robustness after 1000 bending cycles but also does not delaminate from the substrate when subjected to the 3M tape test, exhibiting excellent flexibility and adhesion to substrate. By comparing with the annealed interface of the double-layer Cu/Bi2Te3 film, the interface of the heat-treated Al/Bi2Te3 film has almost no element diffusion, demonstrating high interfacial thermal stability. Moreover, a thermoelectric temperature sensor based on the Al thin-film electrode is prepared. The sensitivity of the annealed sensor is still linear, and it can stably monitor the temperature variation, showing high reliability. This discovery could provide a facile and effective strategy to achieving highly reliable thermoelectric devices and flexible electronic devices without any additional diffusion barriers

Comparison of ablation experiment results of models on hazardous chemical vehicle dataset.

Comparison of ablation experiment results of models on hazardous chemical vehicle dataset.</p

Experimental parameter configuration.

Hazardous chemical vehicles are specialized vehicles used for transporting flammable gases, medical waste, and liquid chemicals, among other dangerous chemical substances. During their transportation, there are risks of fire, explosion, and leakage of hazardous materials, posing serious threats to human safety and the environment. To mitigate these possible hazards and decrease their probability, this study proposes a lightweight object detection method for hazardous chemical vehicles based on the YOLOv7-tiny model.The method first introduces a lightweight feature extraction structure, E-GhostV2 network, into the trunk and neck of the model to achieve effective feature extraction while reducing the burden of the model. Additionally, the PConv is used in the model’s backbone to effectively reduce redundant computations and memory access, thereby enhancing efficiency and feature extraction capabilities. Furthermore, to address the problem of performance degradation caused by overemphasizing high-quality samples, the model adopts the WIoU loss function, which balances the training effect of high-quality and low-quality samples, enhancing the model’s robustness and generalization performance. Experimental results demonstrate that the improved model achieves satisfactory detection accuracy while reducing the number of model parameters, providing robust support for theoretical research and practical applications in the field of hazardous chemical vehicle object detection.</div

Training results of YOLOv7-tiny model and G-YOLO model on hazardous materials vehicle dataset.

(a) Precision Curves Comparison; (b) Recall Curves Comparison.</p

Intersection and juxtaposition of anchor frame and target frame.

Intersection and juxtaposition of anchor frame and target frame.</p

Validation instances of YOLOv7-tiny model and G-YOLO model on traffic detection dataset.

(a) illustrates the validation instances of the YOLOv7-tiny model; (b) illustrates the validation instances of the G-YOLO model.</p

Display of selected datasets.

(a)(b)(c)shows a selection of images from the Dangerous Materials Vehicles dataset. Reprinted from [] under a CC BY license, with permission from [Pengcheng Zhu], original copyright [2023].</p