Traditional product design evaluation processes are resource-intensive and time-consuming, resulting in unsustainably higher costs and longer lead times. Therefore, sustainable product design evaluation has become an increasingly crucial aspect of product design, focusing on creating a high-efficiency, high-reliability, and low-carbon-emission approach. In this study, we proposed an integrated approach that combines manual design evaluation based on the analytic hierarchy process (AHP) with an automatic design evaluation based on a ResNet-50 network in order to develop a sustainable design evaluation method. First, the evaluation level and indicators for the shape design of a tail-light were defined using the AHP. We followed this by establishing a determination matrix and weight coefficients for the design indicators to create a manual design evaluation model. Second, tail-light shape image datasets were manually annotated based on the evaluation indicators, and design datasets were constructed. The ResNet-50 algorithm was introduced to train the datasets, and the automatic evaluation model for product design was constructed through training and tuning. Finally, we validated the feasibility and effectiveness of the product design evaluation method, which was based on AHP and ResNet-50, by comparing the results obtained using both manual design and automatic design evaluations. The results showed that the proposed sustainable product design evaluation model provides an efficient and reliable method for evaluating product design, improves the decision-making process, and empowers the design and development process. The model enhances resource efficiency and economic sustainability.

With the development of the intelligent vision industry, ship detection and identification technology has gradually become a research hotspot in the field of marine insurance and port logistics. However, due to the interference of rain, haze, waves, light, and other bad weather, the robustness and effectiveness of existing detection algorithms remain a continuous challenge. For this reason, an improved YOLOv8n algorithm is proposed for the detection of ship targets under unforeseen environmental conditions. In the proposed method, the efficient multi-scale attention module (C2f_EMAM) is introduced to integrate the context information of different scales so that the convolutional neural network can generate better pixel-level attention to high-level feature maps. In addition, a fully-concatenate bi-directional feature pyramid network (Concatenate_FBiFPN) is adopted to replace the simple superposition/addition of feature map, which can better solve the problem of feature propagation and information flow in target detection. An improved spatial pyramid pooling fast structure (SPPF2+1) is also designed to emphasize low-level pooling features and reduce the pooling depth to accommodate the information characteristics of the ship. A comparison experiment was conducted between other mainstream methods and our proposed algorithm. Results showed that our proposed algorithm outperformed other models by achieving 99.4% of accuracy, 98.2% of precision, 98.5% of recall, 99.1% of mAP@.5, and 85.4% of mAP@.5:.95 on the SeaShips dataset.

Biologically inspired product design (BIPD) inherently encompasses the concept of sustainability. It acquires inspiration from natural organisms, and the references in aspects such as form, structure, and function typically contribute to efficient resource utilization and environmentally friendly coexistence. However, past studies have mainly evaluated from the perspective of designers and researchers, which is relatively subjective. It is difficult to meet the real needs of industry and market. At the same time, the method of establishing indicators is not scientific enough, and the importance of indicators is not ranked. This research integrates the concept of sustainable design into the BIPD evaluation system, comprehensively considering the evaluation indices of different stakeholders such as sustainable designers, industrial designers, and users and decision-makers of design companies. By employing the analytic hierarchy process, a complete and systematic evaluation index model is constructed. This model can comprehensively and accurately screen and evaluate design proposals during the conceptual design stage of BIPD. Through this approach, it effectively averts resource waste caused by incorrect decisions in the production process, optimizes resource allocation, meets user requirements and vigorously promotes the sustainable development of BIPD throughout its entire life cycle.