Since the camouflaged object is highly similar to the background， it is easily confused by background features， making it difficult to distinguish boundary information and extract object features. Current mainstream Camouflaged Object Detection （COD） algorithms mainly study the camouflage object itself and its boundaries， ignoring relationship between the image background and the object， and the detection results are not ideal in complex scenes. To this end， in order to explore potential connection between background and object， an camouflaged object detection algorithm by mining boundaries and background was proposed， called I2DNet （Indirect to Direct Network）. The algorithm consists of five parts： in the encoder， the initial raw data was processed； in the Boundary-guided feature Extracting and Mining Framework （BEMF）， more refined boundary features were extracted through feature processing and feature mining； in the Latent-feature Exploring Framework based on Background guidance （LEFB）， more salient features were explored through multi-scale convolution while based on attention， the Hybrid Attention Module （HAM） was designed to enhance selection of background features； in the Information Supplement Module （ISM）， the detailed information lost during feature processing was made up； in the Multi-task Co-segmentation Decoder （MCD）， the features extracted from different tasks and modules were fused efficiently and the final prediction results were output. Experimental results show that the proposed algorithm is better than the other 15 state-of-the-art models on three widely used datasets； especially on CAMO dataset， the proposed algorithm has the mean absolute error index dropped to 0.042.

In response to the issue of significantly reduced detection performance of face detection models in low-light conditions， a low-light face detection method based on image enhancement was developed. Firstly， image enhancement techniques were applied to preprocess low-light images， enhancing the effective facial features. Secondly， an attention mechanism was introduced after the model’s backbone network to increase the network’s focus on facial regions and reduce the negative impact of non-uniform lighting and noise simultaneously. Furthermore， an attention-based bounding box loss function — Wise Intersection over Union （WIoU） was incorporated to improve the network’s accuracy in detecting low-quality faces. Finally， a more efficient feature fusion module was used to replace the original model structure. Experimental results on the low-light face dataset DARK FACE compared to the original YOLOv7 model indicate that the improved method achieves an increase of 2.4 percentage points in average detection precision AP@0.5 and an increase of 1.4 percentage points in mean value of average precision AP@0.5：0.95， all without introducing additional parameters or computational complexity. Additionally， the results on two other low-light face datasets confirm the effectiveness and robustness of the proposed method， approving the applicability of the method for low-light face detection in diverse scenarios.