RESUMO
To tackle the challenges of weak sensing capacity for multi-scale objects, high missed detection rates for occluded targets, and difficulties for model deployment in detection tasks of intelligent roadside perception systems, the PDT-YOLO algorithm based on YOLOv7-tiny is proposed. Firstly, we introduce the intra-scale feature interaction module (AIFI) and reconstruct the feature pyramid structure to enhance the detection accuracy of multi-scale targets. Secondly, a lightweight convolution module (GSConv) is introduced to construct a multi-scale efficient layer aggregation network module (ETG), enhancing the network feature extraction ability while maintaining weight. Thirdly, multi-attention mechanisms are integrated to optimize the feature expression ability of occluded targets in complex scenarios, Finally, Wise-IoU with a dynamic non-monotonic focusing mechanism improves the accuracy and generalization ability of model sensing. Compared with YOLOv7-tiny, PDT-YOLO on the DAIR-V2X-C dataset improves mAP50 and mAP50:95 by 4.6% and 12.8%, with a parameter count of 6.1 million; on the IVODC dataset by 15.7% and 11.1%. We deployed the PDT-YOLO in an actual traffic environment based on a robot operating system (ROS), with a detection frame rate of 90 FPS, which can meet the needs of roadside object detection and edge deployment in complex traffic scenes.
RESUMO
Environmental radiocarbon (14C) monitoring is gaining importance in China due to the recent rapid development of the nuclear industry. In this study, synthetic benzoic acid samples with the fingerprint of environmental 14C were generated by using the synthesis method, and the specific activity of 14C was determined using the liquid scintillation counting (LSC) method. The ultra-performance liquid chromatography (UPLC) analysis was performed in chemical properties test and results showed that the mean assay and yield of synthetic benzoic acid were 91.8% ± 2.4% and 84.5% ± 2.0%, respectively. Under adopted LSC counting conditions, the minimum detectable activity (MDA) of 64 Bq/kg C was achieved in our laboratory. The method's linearity was examined using a series of spiked samples, along with the 14C-free samples. The results showed a wide linear range up to 2000 Bq/kg C. Reproducible results were obtained from three batches of experiments with deviations in the intra-group and inter-group of 0.38%-3.06% and 1.24%-3.55%, respectively. Long-term evaluation of the system was found to be very stable (over 5 months storage) with the relative standard deviations of <1%. In addition, field applications in the vicinity of a nuclear power plant demonstrated that 14C data measured by our method was consistent with the accelerator mass spectrometry (AMS) method, suggesting the method's precision can meet the requirement of 14C monitoring near the nuclear facility. This is the first study to report the use of synthetic benzoic acid in environmental 14C monitoring, and it provides a new approach for improving the environmental 14C monitoring network.