Object detection is an important aspect for autonomous driving vehicles (ADV), which may comprise of a machine learning model that detects a range of classes. As the deployment of ADV widens globally, the variety of objects to be detected may increase beyond the designated range of classes. Continual learning for object detection essentially ensure a robust adaptation of a model to detect additional classes on the fly. This study proposes a novel continual learning method for object detection that learns new object class(es) along with cumulative memory of classes from prior learning rounds to avoid any catastrophic forgetting. The results of PASCAL VOC 2007 have suggested that the proposed ER method obtains 4.3% of mAP drop compared against the all-classes learning, which is the lowest amongst other prior arts.

Deep learning models have revealed outstanding performance on image classification and object detection tasks. However, there is a crucial drop in performance when they are subject to learn new data incrementally in the absence of previous training data. They suffer from catastrophic forgetting—abrupt drop in performance. This phenomenon affects the implementation of artificial intelligence in practical scenarios. To overcome catastrophic forgetting, the previous method has either saved previous data in memory or generated the previous data. However, these methods are computationally complex and infeasible for real-time applications. In this article, we proposed the YOLOv3 as an object detection framework for incremental learning. A knowledge distillation loss is introduced for the prediction of previously learned knowledge without utilizing previous training data. Consequently, these predictions are updated while learning the current model. Experimental results on the Pascal VOC2007 indicate that the proposed method significantly improved the mean average precision up to 74% for two classes in comparison to the state-of-the-art methods.

This article presents an indoor positioning system based on camera and ultrasonic sensors mounted on a pair of glasses that is specifically designed for visually impaired individuals. The proposed system incorporates a recognition algorithm that is able to recognize certain color-coded markers with a detectable range of 15 m in real time on a quad-core embedded processor. In addition, microultrasonic transducers are implemented to detect obstacles in front of the glasses. The positioning system and obstacle detection can be adjusted to work independently or in parallel. The sensor data are then transmitted to a mobile phone to be processed. The results show that the proposed system is able to recognize markers and detect obstacles with low complexity and power consumption.

Active matrix organic light-emitting diode (AMOLED) display is an emerging technology that has been widely used in mobile devices. It incorporates a varying power-demand character, which is particularly inefficient for displaying bright colors. This paper presents a power-saving scheme for AMOLED display based on the pixel dimming transformation. Initially, the proposed method suppresses luminance and reconstructs colors of any overexposed region. Then, each pixel is transformed using coefficients derived from the SSIM metric, of which the set point is determined from the prior image. Weighted coefficients are employed specifically for high-frequency visible spectra. According to the measurements of three datasets on a smartphone with AMOLED display, up to 75% of displaying power can be reduced with mean visual saliency index of 0.955.

As an emissive display, the active matrix organic light emitting diodes (AMOLED) endures lower power efficiency at the higher level of pixel intensities. In the existing techniques, the power consumption is lowered with significant loss of details and hue alterations. This paper proposes a hue-preserving pixel dimming technique using subtractive coefficients based on the decomposed hue-saturation-value (HSV) color map that reduces the power consumption on AMOLED displays. An entropy-based scene detection is adopted to maintain the computational efficiency of the proposed method on video input. Experimental results on a 5.5” 1080p AMOLED displays show that the proposed method conserves up to 73% of the displaying power with high perceptual qualities as compared to the existing methods.