Weakly supervised object detection (WSOD) in remote sensing images (RSIs) aims to detect high-value targets by solely utilizing image-level category labels; however, two problems have not been well addressed by existing methods. Firstly, the seed instances (SIs) are mined solely relying on the category score (CS) of each proposal, which is inclined to concentrate on the most salient parts of the object; furthermore, they are unreliable because the robustness of the CS is not sufficient due to the fact that the inter-category similarity and intra-category diversity are more serious in RSIs. Secondly, the localization accuracy is limited by the proposals generated by the selective search or edge box algorithm. To address the first problem, a segment anything model (SAM)-induced seed instance-mining (SSIM) module is proposed, which mines the SIs according to the object quality score, which indicates the comprehensive characteristic of the category and the completeness of the object. To handle the second problem, a SAM-based pseudo-ground truth-mining (SPGTM) module is proposed to mine the pseudo-ground truth (PGT) instances, for which the localization is more accurate than traditional proposals by fully making use of the advantages of SAM, and the object-detection heads are trained by the PGT instances in a fully supervised manner. The ablation studies show the effectiveness of the SSIM and SPGTM modules. Comprehensive comparisons with 15 WSOD methods demonstrate the superiority of our method on two RSI datasets.

Oriented object detection (OOD) can recognize and locate various objects more precisely than horizontal object detection; however, two problems have not been satisfactorily resolved so far. Firstly, the absence of interactions between the classification and regression branches leads to inconsistent performance in the two tasks of object detection. Secondly, the traditional convolution operation cannot precisely extract the features of objects in extremely aspect ratio in remote sensing images (RSIs). To address the first problem, the task-aligned detection module (TADM) and the task-aligned loss function (TL) are proposed in this paper. On the one hand, a spatial probability map and a spatial offset map are inferred from the shared features in the TADM and separately incorporated into the classification and regression branches to obtain consistency in the two tasks. On the other hand, the TL combines employing the generalized intersection over union (GIoU) metric with classification loss to further enhance the consistency in the two tasks. To address the second problem, a two-stage detection framework based on alignment convolution (TDA) is proposed. The features extracted from the backbone network are refined through alignment convolution in the first stage, and the final OOD results are inferred from refined features in the second stage. The ablation study verifies the effectiveness of the TADM, TL, and TDA. The comparisons with other advanced methods, on two RSI benchmarks, demonstrate the overall effectiveness of our method.

The You Only Look Once (YOLO) object detection model has been widely applied to electric power operation violation identification, owing to its balanced performance in detection accuracy and inference speed. However, it still faces the following challenges: (1) insufficient detection capability for irregularly shaped objects; (2) objects with low object-background contrast are easily omitted; (3) improving detection accuracy while maintaining computational efficiency is difficult. To address the above challenges, a novel real-time object detection model is proposed in this paper, which introduces three key innovations. To handle the first challenge, an edge perception cross-stage partial fusion with two convolutions (EPC2f) module that combines edge convolutions with depthwise separable convolutions is proposed, which can enhance the feature representation of irregularly shaped objects with only a slight increase in parameters. To handle the second challenge, an adaptive combination of local and global features module is proposed to enhance the discriminative ability of features while maintaining computational efficiency, where the local and global features are extracted respectively via 1D convolutions and adaptively combined by using learnable weights. To handle the third challenge, a parameter sharing of a multi-scale detection heads scheme is proposed to reduce the number of parameters and improve the interaction between multi-scale detection heads. The ablation study on the Ali Tianchi competition dataset validates the effectiveness of three innovation points and their combination. EAP-YOLO achieves the mAP@0.5 of 93.4% and an mAP@0.5–0.95 of 70.3% on the Ali Tianchi Competition dataset, outperforming 12 other object detection models while satisfying the real-time requirement.

In order to solve the problem that steel surface defects are easily covered or submerged by other objects or noise, this paper proposes an open–closed transformation algorithm which can eliminate or weaken multiple noises. In the case of a small number of samples, this paper establishes a super-resolution generative adversarial neural network to achieve the enhancement of sample data. For avoiding unrealistic image defects caused by cuts or brightness variations, an enhancement method is given which incorporates the original defective high-frequency information into classical image fusion methods, such as rotation and error slicing. Experimental results show that the accuracy of the proposed denoising method reaches over 90%, which is more than 2.6% of that of the most primitive classification network. To compare with existing denoising methods, the denoising method proposed in this paper not only has higher accuracy, faster denoising speed, and stronger anti-interference ability, but also has better adaptation to the environment. This research will provide a new solution method for the denoising of multi-noise phenomena in multiple different environments.