## 1 Introduction  
Road infrastructure is pivotal for the socio-economic development and general prosperity of societies, acting as the backbone of connectivity and transportation. Maintaining the integrity and longevity of these road networks is imperative for transportation authorities globally. A major impediment faced by these bodies is the swift detection and subsequent classification of road anomalies like potholes, cracks, and other deformities. Such irregularities, if left unchecked, can result in accidents, amplify vehicular wear and tear, and escalate road maintenance expenditures. Historically, the detection of such road maladies predominantly depended upon human inspection, a method that, despite its earnest intent, is laborious, time-intensive, and susceptible to oversights. The evolution of camera and laser technologies [1] has ushered in a new era, allowing for the capture of road images in high resolution, even while in transit. This has resulted in the accumulation of expansive datasets of road imagery. However, the true challenge surfaces in the meticulous identification and categorization of road distress within these extensive datasets [2]. Addressing the intricacies of pavement management, [PERSON] et al. architected a model harnessing the prowess of multi-classification machine learning algorithms, aiming for the judicious application of treatments to distinct road segments. This model showcases formidable accuracy, predicting both untreated and treated segments with high precision [3]. Numerous innovators have further enhanced this domain. [PERSON] introduced a novel intersection of mathematical modeling with Genetic Algorithm and Artificial Neural Networks, aspiring to deduce the Pavement Quality Index of flexible pavements [4]. [PERSON] collaborators ventured into machine learning, aiming to gauge the repercussions of excessive traffic weight on asphalt pavement's life, employing a random survival forest algorithm [5]. In yet another groundbreaking endeavor, [PERSON] et al. presented a hybrid model, demonstrating superior accuracy in estimating the International Roughness Index of rigid pavements compared to traditional techniques [6]. In this expansive realm, researchers like [PERSON] et al. probed the potential of intelligent image analysis techniques for pavement assessment, elucidating that these avant-garde systems could substantially economize both cost and time [7]. [PERSON] and his team showcased a fusion LSTM-BPNN model that surpassed its predecessors in forecasting pavement performance [8]. Moreover, a slew of reviews and studies from researchers like [PERSON] et al. and [PERSON] et al. spotlighted the myriad applications and predictive capacities of machine learning and artificial intelligence within pavement engineering [9; 10; 11]. Despite these advancements, road distress identification, with its intricate nuances, often remains elusive to conventional image classification models. A pivotal reason is the sparse distribution of pixels that characterize road distress juxtaposed against a predominant background--creating a stark imbalance and a unique challenge in image analysis. To counter these challenges, this paper details a novel model rooted in sparse perception for road distress classification. The model's architecture rests on two foundational pillars: a sparse feature extraction module and a domain-specific loss function. The sparse feature extraction module, leveraging the potential of dilated convolution, is adept at capturing sparse features across various scales, creating a comprehensive representation of road conditions. Meanwhile, our loss function, conceived with a meticulous understanding of the pavement domain, ensures the consistent sparsity of the feature space. Rigorous validation on an authentic dataset affirmed the potency of our model, recording remarkable \(F1\) scores and signifying a profound edge over conventional methods.