Sustainable and practical personal mobility solutions for campus environments have traditionally revolved around the use of bicycles, or provision of pedestrian facilities. However many campus environments also experience traffic congestion, parking difficulties and pollution from fossil-fuelled vehicles. It appears that pedal power alone has not been sufficient to supplant the use of petrol and diesel vehicles to date, and therefore it is opportune to investigate both the reasons behind the continual use of environmentally unfriendly transport, and consider potential solutions. This paper presents the results from a year-long study into electric bicycle effectiveness for a large tropical campus, identifying barriers to bicycle use that can be overcome through the availability of public use electric bicycles

Aiming to address the current shortcomings of the existing safety helmet wearing detection algorithms, including a slow reasoning speed, a large model size, and high hardware requirements, this study proposes an improved safety helmet wearing detection network named YOLOv5-SN, which is suitable for embedded deployment on Jetson Nano. First, the backbone of the YOLOv5 network is modified using the model lightweight method introduced by ShuffleNetV2. Next, the model size and number of parameters in the trained model are reduced to about one-tenth of those of the YOLOv5 network, and the reasoning speed is improved by 72 ms/f when tested on Jetson Nano. Then, the modified model is optimized using the quantification and layer fusion operations, further reducing the computing power and accelerating the reasoning speed. Finally, the YOLOv5-SN network is obtained by improving the YOLOv5 model, and the optimized model is deployed on Jetson Nano for testing. The average reasoning speed of the YOLOv5s-SN network reaches 32.2 ms/f, which is 84.7 ms/f faster compared to that of the YOLOv5s model. This demonstrates an obvious advantage of the proposed model in reasoning speed compared to the existing YOLOv5 models. Finally, the proposed model can perform real-time and effective target detection on the Jetson Nano embedded terminal.

No abstract

No abstract