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Target detection in adverse conditions such as low illumination has always been a challenging. The factors of low light and fog can lead to reduced visibility and increased noise in images, significantly disrupting the precision of object detection. To address these issues, this paper proposed and integrated a low-light image enhancement network for machine vision perception, MVP-Net, with the YOLOv3 object detection network to construct an end-to-end enhancement detection framework, MVPYOLO. MVP-Net employed inverse mapping network technology to transform conventional RGB images into pseudo-RAW image feature space and introduced a pseudo-ISP enhancement network, DOISP, for image enhancement. The objective of MVP-Net is to harness the potential advantages of RAW images in object detection while overcoming the limitations encountered in their direct application. The model has outperformed previous works on multiple real-world low-light datasets and is adaptable to detectors with various architectures. Its end-to-end detection framework achieves a mAP(50%) metric of 78.3%, an improvement of 1.85% over the YOLO detectors. [ABSTRACT FROM AUTHOR]

The weakly supervised methods for image semantic segmentation using the image-level labels are a relatively popular research direction. The class activation maps generation approach is the most commonly used approach in these researches. Due to the sparsity of class activation maps, the accuracy of discriminative regions is generally low. To address these problems, this paper proposed an improved weakly supervised image learning method based on the Transformer network. Firstly, it introduced a spatial attention interaction layer to extend the coverage of the class activation maps. Secondly, it designed an attention adaptive module to guide the model to enhance the class response in weak regions. In particular, it constructed an adpative cross domain to improve the model classification performance during class generation. The method achieves the accuracies of 73.5% and 73.0% on the Pascal VOC 2012 validation sets and test sets, respectively. The experimental results prove that the refined Transformer network learning method can improve the image semantic segmentation performance of weakly supervised method. [ABSTRACT FROM AUTHOR]

There are many fields choose to fuse LiDAR point cloud and camera images for applying. The accurate extrinsic calibration is a prerequisite for fusing their information. The point cloud feature extraction is a key step in extrinsic calibration. However, the low resolution and low quality of point clouds can affect the accuracy of calibration results. To address these problems, this paper proposed a LiDAR and camera extrinsic calibration method based on edge correlation point cloud. Firstly, it extracted the edge correlation point cloud of the calibration plate by using dual echoes. Then, it obtained the corner points from the edge correlation point cloud by the optimization method, where the corner points were compatible with the actual calibrated plate size. Finally, it matched the corner points in the point cloud with corner points in the image, and solved the extrinsic parameter between the LiDAR and the camera by the multi-point perspective method. The experimental results show that the reprojection error of the method is 1. 602 px, which is lower than that of comparison methods, verifying the effectiveness and accuracy of the method. [ABSTRACT FROM AUTHOR]