RESUMEN
With the advancement of society, ensuring the safety of personnel involved in municipal construction projects, particularly in the context of pandemic control measures, has become a matter of utmost importance. This paper introduces a security measure for municipal engineering, combining deep learning with object detection technology. It proposes a lightweight artificial intelligence (AI) detection method capable of simultaneously identifying individuals wearing masks and safety helmets. The method primarily incorporates the ShuffleNetv2 feature extraction mechanism within the framework of the YOLOv5s network to reduce computational overhead. Additionally, it employs the ECA attention mechanism and optimized loss functions to generate feature maps with more comprehensive information, thereby enhancing the precision of target detection. Experimental results indicate that this algorithm improves the mean average precision (mAP) value by 4.3%. Furthermore, it reduces parameter and computational loads by 54.8% and 53.8%, respectively, effectively striking a balance between lightweight operation and precision. This study serves as a valuable reference for research pertaining to lightweight target detection in the realm of municipal construction safety measures.
Asunto(s)
Inteligencia Artificial , Dispositivos de Protección de la Cabeza , Humanos , Algoritmos , Pandemias , Medidas de SeguridadRESUMEN
Recently, rapid progress in the power conversion efficiency for organic solar cells (OSCs) is achieved due to the phenomenal development of the nonfullerene electron acceptors. In addition to the pairing electron donors, conjugated donor-acceptor copolymers are another key player in the high-efficiency OSCs. Here, the temporal evolution of excited states in a typical copolymer, PM6, was traced by transient absorption spectroscopy. The spectroscopic result implies the formation of two kinetically correlated intrachain species, polaron excitons and intrachain polaron pairs. In the presence of the interchain interaction, these intrachain species quickly convert into interchain polaron pairs on a time scale of few picoseconds. Our findings reveal that the electron transfer mechanisms in PM6-based OSCs substantially depend on the PM6 environment in the bulk heterojunction blends.