Abstract: The disclosure is directed to an image dehazing method and an image dehazing apparatus using the same method. In an aspect, the disclosure is directed to an image dehazing method, and the method would include not limited to: receiving an input image; dehazing the image by a dehazing module to output a dehazed RGB image; recovering image brightness of the dehazed RGB image by a high dynamic range (HDR) module to output an HDR image; and removing reflection of the HDR image by a ReflectNet inference model, wherein the ReflectNet inference model uses a deep learning architecture.

Abstract: An image recognition method, including: obtaining an image to be recognized by an image sensor; inputting the image to be recognized to a single convolutional neural network; obtaining a first feature map of a first detection task and a second feature map of a second detection task according to an output result of the single convolutional neural network, wherein the first feature map and the second feature map have a shared feature; using an end-layer network module to generate a first recognition result corresponding to the first detection task from the image to be recognized according to the first feature map, and to generate a second recognition result corresponding to the second detection task from the image to be recognized according to the second feature map; and outputting the first recognition result corresponding to the first detection task and the second recognition result corresponding to the second detection task.

Abstract: The disclosure is directed to an image dehazing method and an image dehazing apparatus using the same method. In an aspect, the disclosure is directed to an image dehazing method, and the method would include not limited to: receiving an input image; dehazing the image by a dehazing module to output a dehazed RGB image; recovering image brightness of the dehazed RGB image by a high dynamic range (HDR) module to output an HDR image; and removing reflection of the HDR image by a ReflectNet inference model, wherein the ReflectNet inference model uses a deep learning architecture.

Abstract: An image recognition method, including: obtaining an image to be recognized by an image sensor; inputting the image to be recognized to a single convolutional neural network; obtaining a first feature map of a first detection task and a second feature map of a second detection task according to an output result of the single convolutional neural network, wherein the first feature map and the second feature map have a shared feature; using an end-layer network module to generate a first recognition result corresponding to the first detection task from the image to be recognized according to the first feature map, and to generate a second recognition result corresponding to the second detection task from the image to be recognized according to the second feature map; and outputting the first recognition result corresponding to the first detection task and the second recognition result corresponding to the second detection task.

Abstract: In one of the exemplary embodiments, the disclosure is directed to an object detection system including a first type of sensor for generating a first sensor data; a second type of sensor for generating a second sensor data; and a processor coupled to the first type of sensor and the second type of sensor and configured at least for: processing the first sensor data by using a first plurality of object detection algorithms and processing the second sensor data by using a second plurality of object detection algorithms, wherein each of the first plurality of object detection algorithms and each of the second plurality of object detection algorithms include environmental parameters calculated from a plurality of parameter detection algorithms; and determining for each detected object a bounding box resulted from processing the first sensor data and processing the second sensor data.

Abstract: The disclosure is directed to an object detection method using a CNN model and an object detection apparatus thereof. In an aspect, the object detection method includes generating a sensor data; processing the sensor data by using a first object detection algorithm to generate a first object detection result; processing the first object detection result by using a plurality of stages of sparse update mapping algorithm to generate a plurality of stages of updated first object detection result; processing a first stage of the stages of updated first object detection result by using a plurality of stages of spatial pooling algorithm between each of stages of sparse update mapping algorithm; executing a plurality of stages of deep convolution layer algorithm to extract a plurality of feature results; and performing a detection prediction based on a last-stage feature result.

Abstract: In one of the exemplary embodiments, the disclosure is directed to a depth estimation apparatus including a first type of sensor for generating a first sensor data; a second type of sensor for generating a second sensor data; and a processor coupled to the first type of sensor and the second type of sensor and configured at least for: processing the first sensor data by using two stage segmentation algorithms to generate a first segmentation result and a second segmentation result; synchronizing parameters of the first segmentation result and parameters of the second sensor data to generate a synchronized second sensor data; fusing the first segmentation result, the synchronized second sensor data, and the second segmentation result by using two stage depth estimation algorithms to generate a first depth result and a second depth result.

Abstract: The disclosure is directed to an object detection method using a CNN model and an object detection apparatus thereof. In an aspect, the object detection method includes generating a sensor data; processing the sensor data by using a first object detection algorithm to generate a first object detection result; processing the first object detection result by using a plurality of stages of sparse update mapping algorithm to generate a plurality of stages of updated first object detection result; processing a first stage of the stages of updated first object detection result by using a plurality of stages of spatial pooling algorithm between each of stages of sparse update mapping algorithm; executing a plurality of stages of deep convolution layer algorithm to extract a plurality of feature results; and performing a detection prediction based on a last-stage feature result.

Abstract: In one of the exemplary embodiments, the disclosure is directed to a depth estimation apparatus including a first type of sensor for generating a first sensor data; a second type of sensor for generating a second sensor data; and a processor coupled to the first type of sensor and the second type of sensor and configured at least for: processing the first sensor data by using two stage segmentation algorithms to generate a first segmentation result and a second segmentation result; synchronizing parameters of the first segmentation result and parameters of the second sensor data to generate a synchronized second sensor data; fusing the first segmentation result, the synchronized second sensor data, and the second segmentation result by using two stage depth estimation algorithms to generate a first depth result and a second depth result.

Abstract: In one of the exemplary embodiments, the disclosure is directed to an object detection system including a first type of sensor for generating a first sensor data; a second type of sensor for generating a second sensor data; and a processor coupled to the first type of sensor and the second type of sensor and configured at least for: processing the first sensor data by using a first plurality of object detection algorithms and processing the second sensor data by using a second plurality of object detection algorithms, wherein each of the first plurality of object detection algorithms and each of the second plurality of object detection algorithms include environmental parameters calculated from a plurality of parameter detection algorithms; and determining for each detected object a bounding box resulted from processing the first sensor data and processing the second sensor data.