Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground–background imbalance. Fortunately, modification of loss function appears to solve this puzzle once and for all. In this article, we propose a pixel-based adaptive weighted cross-entropy (WCE) loss in conjunction with Jaccard distance to facilitate high-quality pixel-level road crack detection. Our work profoundly demonstrates the influence of loss functions on detection outcomes and sheds light on the sophisticated consecutive improvements in the realm of crack detection. Specifically, to verify the effectiveness of the proposed loss, we conduct extensive experiments on four public databases, that is, CrackForest, AigleRN, Crack360, and BJN260. Compared to the vanilla WCE, the proposed loss significantly speeds up the training process while retaining the performance.

Pixel-level road crack detection has always been a challenging task in intelligent transportation systems. Due to the external environments, such as weather, light, and other factors, pavement cracks often present low contrast, poor continuity, and different sizes in length and width. However, most of the existing studies pay less attention to crack data under different situations. Meanwhile, recent algorithms based on deep convolutional neural networks (DCNNs) have promoted the development of cutting-edge models for crack detection. Nevertheless, they usually focus on complex models for good performance, but ignore detection efficiency in practical applications. In this article, to address the first issue, we collected two new databases (i.e. Rain365 and Sun520) captured on rainy and sunny days respectively, which enrich the data of the open source community. For the second issue, we reconsider how to improve detection efficiency with excellent performance, and then propose our lightweight encoder-decoder architecture termed CarNet. Specifically, we introduce a novel olive-shaped structure for the encoder network, a lightweight multi-scale block and a new up-sampling method in the decoder network. Numerous experiments show that our model can better balance detection performance and efficiency compared with previous models. Especially, on the Sun520 dataset, our CarNet significantly advances the state-of-the-art performance with ODS F-score from 0.488 to 0.514. Meanwhile, it does so with an improved detection speed (104 frames per second) which is orders of magnitude faster than some recent DCNNs-based algorithms specially designed for crack detection.

Existing studies for automatic waveform recognition of overlapping signals have mostly been conducted in a supervised manner. Although demonstrating superior performance in recent years, supervised methods rely heavily on sufficient labeled samples, rely but the acquisition of annotated data is expensive, time-consuming, and sometimes rely infeasible. This shortage drives the need for semi-supervised learning methods, where the unlabeled samples can be fully exploited in the training stage. In addition, multi-instance multi-label (MIML) learning is essentially another weakly supervised learning protocol, and precisely fits the form of the time-frequency images TFIs obtained from the transformation of overlapping signals. In this paper, delving into the MIML learning problem, we leverage the advantage of adversarial training to formulate an effective algorithm MIML-GAN, which is tailored to the MIML problem of overlapping signal waveform recognition. After feeding the TFIs into the network, MIML-GAN approximates the distribution of the training data using the adversarial learning principle. Subsequently, the baglevel prediction can be derived from the instance-level prediction upon the MIML discriminator through adaptive threshold calibration. Specifically, we elaborately studied the global optimality of the MIML-GAN objective function, and extensive simulations are carried out with overlapping signal dataset, validating the ascendancy of the proposed method. Comparative experiments demonstrate that the proposed algorithm possesses promising feature representation capability, and outperforms the existing semi-supervised and supervised signal waveform recognition approaches.

Multi-scale representation plays a critical role in the field of edge detection. However, most of the existing research focuses on one of two aspects: fast training and accurate testing. In this paper, we propose a novel multi-scale method to resolve the balance between them. Specifically, according to multi-stream structures and the image pyramid principle, we construct a down-sampling pyramid network and a lightweight up-sampling pyramid network to enrich the multi-scale representation from the encoder and decoder, respectively. Next, these two pyramid networks and a backbone network constitute our overall architecture, a bi-directional pyramid network (BDP-Net). Extensive experiments show that compared with the state-of-the-art model, our method could improve the training speed by about one time while retaining a similar test accuracy. Especially, under the single-scale test, our approach also reaches human perception (F₁ score of 0.803) on the BSDS500 database.