Abstract. Recently, a large amount of development of Android applications being developed because of an increase in smartphone demand. Social network applications are one of the popular applications. Almost all these applications handle photos or images. Android application developers feel burden on processing images or photos in applications. For this reason, android third party libraries which refine inconvenience are developed. In this paper, we compare and analyze Picasso, Glide, Fresco and AUIL which are android third party libraries used for image processing. Furthermore, we also compare and analyze Volley framework. Our results can help image loading library users choose the appropriate one among them.

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The blind encounter commuting risks, such as failing to recognize and avoid obstacles while walking, but protective support systems are lacking. Acoustic signals at crosswalk lights are activated by button or remote control; however, these signals are difficult to operate and not always available (i.e., broken). Bollards are posts installed for pedestrian safety, but they can create dangerous situations in that the blind cannot see them. Therefore, we proposed an obstacle recognition system to assist the blind in walking safely outdoors; this system can recognize and guide the blind through two obstacles (crosswalk lights and bollards) with image training from the Google Object Detection application program interface (API) based on TensorFlow. The recognized results notify the blind through voice guidance playback in real time. The single shot multibox detector (SSD) MobileNet and faster region-convolutional neural network (R-CNN) models were applied to evaluate the obstacle recognition system; the latter model demonstrated better performance. Crosswalk lights were evaluated and found to perform better during the day than night. They were also analyzed to determine if a client could cross at a crosswalk, while the locations of bollards were analyzed by algorithms to guide the client by voice guidance.

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