Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss

In object detection algorithms, the bounding box regression (BBR) loss directly influences the model's accuracy in predicting object positions. Initially, we conducted simulation experiments on the proposed intersection over union-based loss. Through in-depth analysis, we discovered that the width and height components of the distance term could hinder BBR. To address this, we proposed the AIoU-v1 loss, which decouples the width and height components of the distance term, thereby preventing the suppression of BBR loss by the distance term. Additionally, issues such as the imbalance between sample quantity and sample quality in the dataset, as well as labeling errors, can adversely affect BBR. To tackle these dataset problems, we designed an adaptive dynamic non-monotonic focusing mechanism with strong robustness and wide applicability. Finally, we proposed a post-processing algorithm that combines fusion and non-maximum suppression, resulting in more accurate bounding boxes during the post-processing stage. Our source code and data are available at https://github.com/Wenhua-Zheng/AIOU.

Loss functions, such as the IoU Loss function and the GIoU (Generalized Intersection over Union) Loss function have been put forward to replace regression loss functions commonly used in regression loss calculation. GIoU Loss alleviates the vanishing gradient in the case of the non-overlapping, but it will completely degenerate into the IoU Loss function when bounding boxes overlap totally, which fails to achieve the optimization effect. To solve this problem, some improvements are proposed in this paper on the basis of the GIoU Loss function, taking into account the overlap rate of complete overlap of bounding boxes. In PASCAL VOC data, the experimental results demonstrate that the AP of NGIoU Loss function in the YOLOv4 model is 47.68%, 1.15% higher than that of the GIoU Loss function, and the highest map value is 86.79% in the YOLOv5 model.

Bounding box regression is an important step in the process of object detection, which has a direct impact on the accuracy of model location. The $\ell_{n}$ loss is widely used in bounding box regression, but the criterion of locational accuracy is Intersection over Union (IOU), and there is no strong correlation between them. Although IoU-based loss functions have been proposed successively, such as IoU, GIoU, DIoU and CIoU, which make location loss directly related to evaluation criteria, there are still problems such as slow training convergence speed and inaccurate model location. Based on the IoU loss function, we add normalized corner distance and parameterized width height difference to form a new loss function: LCornerIoU loss. The results of bounding box regression simulation show that LCornerIoU loss is superior to other IoU-based loss functions in terms of convergence speed and positioning accuracy. Experiments on COCO benchmark show that the performance of the Faster R-CNN, RetinaNet, and YOLO-v5s models combined with LCornerIoU loss have consistent performance improvement compared with IoU loss.

Achieving high detection accuracy and high inference speed is important for a pedestrian detection system in self-driving applications. There exists a trade-off between detection accuracy and inference speed in modern convolutional object detectors. In this paper, we propose a novel pedestrian detection system, which leverages spatial attention and a two-level cascade of classification and bounding box regression to balance the trade-off. Our proposed spatial attention module reduces the search space for pedestrians by selecting a small set of anchor boxes for further processing. Furthermore, we present a two-level cascade of bounding box classification and regression and demonstrate its effectiveness for improved accuracy. We demonstrate the performance of our system on 2 public datasets-caltech-reasonable and citypersons; with state-of-art performance. Our ablation studies confirm the usefulness of our spatial attention and cascade modules.

Automatic spondylolisthesis grading from MRIs across modalities using faster adversarial recognition network.

Sugarcane stem node detection is critical for monitoring sugarcane growth, enabling precision cutting, reducing spuriousness, and improving breeding for resistance to downfall. However, in complex field environments, sugarcane stem nodes often suffer from reduced detection accuracy due to background interference and shadowing effects. For this reason, this paper proposes an improved sugarcane stem node detection model based on YOLO11. This study incorporates the ASF-YOLO (Attentional Scale Sequence Fusion based You Only Look Once) mechanism to enhance the feature fusion layer of YOLO11. Additionally, a high-resolution detection layer, P2, is integrated into the fusion module to improve the model’s ability to detect small objects—particularly sugarcane stem nodes—and to better handle multi-scale feature representations. Secondly, to better align with the P2 small-object detection layer, this paper adopts a shared convolutional detection head named LSDECD (Lightweight Shared Detail-Enhanced Convolutional Detection Head), which can better deal with small target detection while reducing the number of model parameters through parameter sharing and detail-enhanced convolution. Using soft-NMS (non-maximum suppression) to replace the original NMS and combining with Shape-IoU, a bounding box regression method that focuses on the shape and scale of the bounding box itself, makes the bounding box regression more accurate, and solves the problem of the impact of detection caused by occlusion and illumination. Finally, to address the increased complexity introduced by the addition of the P2 detection layer and the replacement of the detection head, channel pruning is applied to the model, effectively reducing its overall complexity and parameter count. The experimental results show that the model before pruning has 96.1% and 53.2% mean average precision mAP50 and mAP50:95, respectively, which are 11.9% and 11.1% higher than the original YOLO11n, and the model after pruning also has 10.8% and 9.3% higher than the original YOLO11n, respectively, and the number of parameters is reduced to 279,778, and model size is reduced to 1.3MB. The computational cost decreased from 11.6 GFlops to 6.6 GFlops.

Object detection is a fundamental task in computer vision. Bounding box regression (BBR) losses are critical to detector performance. However, evaluation measures that rely on the Intersection over Union (IoU) between the predicted and ground truth boxes are highly sensitive to positional deviations, which can hinder optimization. To alleviate this issue, we propose ATDIoU, a novel arctangent-differential loss for bounding-box regression. ATDIoU computes distance similarity between a predicted and a ground truth box by modeling the distances between their corresponding vertices as a two-dimensional arctangent differential distribution (ATD). This arctangent differential-based design mitigates bounding box drift and reduces sensitivity to localization errors. As a result, it guides the model to learn target positions more effectively. We evaluate ATDIoU by integrating it into YOLOv6 and conducting experiments on PASCAL VOC and VisDrone2019. The results demonstrate that ATDIoU yields improvements of 1.4% and 0.7% in mean average precision (mAP) relative to MPDIoU.

The &#x201C;shared head for classification and localization&#x201D; (sibling head) has been leading the fashion of the object detection community over the past years. However, the article by Song <i>et al</i>. (2020) found that there is a spatial misalignment between the two object functions of sibling head and proposed an operator called task-aware spatial disentanglement (TSD) to decouple the classification and regression from the spatial dimension by generating two disentangled proposals. But there are two problems in applying TSD to detecting rotated objects. The first problem is that the deformable pooling with the disentangled proposals in TSD is not rotation-invariant. The second problem is that the angle offset is not considered when getting the disentangled proposals. To address the above problems, we propose a rotation-invariant TSD (RITSD) method. The proposed method adopts the rotation-invariant deformable pooling and angle offset learning when getting region-of-interest (RoI) features. Considering the rotated anchor increases the computational burden and training time, we use a three-stage backbone that first utilizes horizontal proposals to obtain rotated proposals and then obtains the predicted rotated bounding boxes. Finally, detailed ablation studies on our collected dataset and HRSC2016 were performed to demonstrate the feasibility and superiority of our proposed method. From the heatmap, we find the features of near bow and stern areas are beneficial for classification and the side areas of ships are good at bounding box regression.

IMSiam: IoU-aware Matching-adaptive Siamese network for object tracking

Object detection in aerial images having many practical applications in real life is becoming popular along with the surging development of deep learning and UAVs (Unmanned Aerial Vehicles). However, adverse weather conditions such as rain, night, and fog might reduce the quality of input images and significantly affect the performance of many perfectly trained detectors (detectors are trained in clear weather conditions). Moreover, object detection in aerial images often seeks high accuracy, while well-known object detection methods use horizontal bounding boxes to represent the object's location. These issues raise the inconsistency between classification and bounding box regression. Understanding the need for practical solutions, we create two experimental Fog datasets based on the original DOTA dataset to provide the in-depth analysis of fog density on multiple well-established oriented object detectors, namely Gliding Vertex, R3Det and ReDet. Furthermore, our training resources achieve promising results, flexibly ensemble to other methods to enhance models' performance and adapt to many adverse weather conditions.

Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection. It overcomes the inherent limitations of IoU-based NMS variants to provide a more stable, consistent predictor of bounding box clustering by using a normalized Manhattan Distance inspired proximity metric to represent bounding box clustering. Unlike Greedy and Soft NMS, it does not rely solely on classification confidence scores to select optimal bounding boxes, instead selecting the box which is closest to every other box within a given cluster and removing highly confluent neighboring boxes. Confluence is experimentally validated on the MS COCO and CrowdHuman benchmarks, improving Average Precision by 0.2--2.7% and 1--3.8% respectively and Average Recall by 1.3--9.3 and 2.4--7.3% when compared against Greedy and Soft-NMS variants. Quantitative results are supported by extensive qualitative analysis and threshold sensitivity analysis experiments support the conclusion that Confluence is more robust than NMS variants. Confluence represents a paradigm shift in bounding box processing, with potential to replace IoU in bounding box regression processes.

Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that $IoU$ can be directly used as a regression loss. However, $IoU$ has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the weaknesses of $IoU$ by introducing a generalized version as both a new loss and a new metric. By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.

In this paper, we investigate a more efficient IoU loss based bounding box localization mechanism on top of end-to-end target detection frameworks to further improve the regression accuracy of object detection methods. Aiming at the limited spatial perception ability of deep convolution features, this paper proposes an optimization strategy by combining shallow spatial feature feed-forward mechanism (SSFF) with focal IoU loss function for bounding box regression tasks in target detection. This strategy firstly constructs a channel to transfer important location information in shallow spatial features to deep spatial features to reduce the loss of spatial details. Secondly, it constructs a focal IoU loss function based on the IoU loss, which dynamically adjusts loss weights according to the regression difficulty of different bounding boxes, to improve the positioning ability of regression networks on difficult bounding boxes. The experimental results show that the proposed methods can effectively improve the regression accuracy of target detection models.

An object detector based on convolutional neural network (CNN) has been widely used in the field of computer vision because of its simplicity and efficiency. The average accuracy of CNN model detection results in the object detector is greatly affected by the loss function. The precision of the localization algorithm in the loss function is the main factor affecting the result. Based on the complete intersection over union (CIoU) loss function, an improved penalty function is proposed to improve the localization accuracy. Specifically, the algorithm more comprehensively considers matching bounding boxes between prediction with ground truth, using the proportional relationship of the aspect ratio from both bounding boxes. Under the same aspect ratio of the two bounding boxes, the influence factors of the prediction box on localization accuracy were considered. In this way, the function of the penalty function is strengthened, and localization accuracy of the network model improved. This loss function is called Improved CIoU (ICIoU). Experiments on the Udacity, PASCAL VOC, and MS COCO datasets have demonstrated the effectiveness of ICIoU in improving localization accuracy of network models by using the one-stage object detector YOLOv4. Compared with CIoU, the proposed ICIoU improved average precision (AP) by 0.57% and AP75 by 0.12% on Udacity, AP by 0.26% and AP75 by 1.28% on PASCAL VOC, and AP by 0.06% and AP75 by 0.65% on MS COCO.

Accurate bounding-box regression with distance-IoU loss for visual tracking